Modelling_Normal_OHE.ipynb

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      "name": "Modelling_Normal_OHE.ipynb",
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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ashishthomaschempolil/Santander-Customer-Satisfaction/blob/new_1/Modelling_Normal_OHE.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QVQr_7USbczr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set(palette='muted',style='whitegrid')\n",
        "import sklearn\n",
        "from sklearn import metrics,ensemble,model_selection,linear_model,tree,calibration\n",
        "import xgboost as xgb\n",
        "import lightgbm as lgb\n",
        "import scipy\n",
        "import random\n",
        "from tqdm import tqdm\n",
        "import os\n",
        "np.random.seed(13154)"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hRB4PxAYa5xu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "6c24df4d-666c-419a-989d-1d5df5f5b2cf"
      },
      "source": [
        "file = [k for k in os.listdir('/content/drive/My Drive/Santander_Customer_Satisfaction') if (('train' in k)|('test' in k))&('X' not in k)] #getting all the necessary files for modelling\n",
        "file"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['train_Normal.pkl',\n",
              " 'test_Normal.pkl',\n",
              " 'train_Normal with Response encoding.pkl',\n",
              " 'test_Normal with Response encoding.pkl',\n",
              " 'train_Normal with One Hot Encoding.pkl',\n",
              " 'test_Normal with One Hot Encoding.pkl',\n",
              " 'train_Log Transformed.pkl',\n",
              " 'test_Log Transformed.pkl',\n",
              " 'train_Log Transformed with Response encoding.pkl',\n",
              " 'test_Log Transformed with Response encoding.pkl',\n",
              " 'train_Log Transformed with One Hot Encoding.pkl',\n",
              " 'test_Log Transformed with One Hot Encoding.pkl']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mV0hmG66S7ZC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ac72fc0f-8897-4185-c40c-83d65a972789"
      },
      "source": [
        "file_path = '/content/drive/My Drive/Santander_Customer_Satisfaction/'\n",
        "train_file = file_path+'train_Normal with One Hot Encoding.pkl' #nomral ohe\n",
        "test_file = file_path+  'test_Normal with One Hot Encoding.pkl'\n",
        "dataset= \"Normal with OHE\"\n",
        "train = pd.read_pickle(train_file)\n",
        "test = pd.read_pickle(test_file)\n",
        "X_train = train.drop(['ID','TARGET'],axis=1)\n",
        "y_train = train['TARGET'].values\n",
        "X_test = test.drop('ID',axis=1)\n",
        "del train,test\n",
        "X_train,X_val,y_train,y_val = model_selection.train_test_split(X_train,y_train,stratify=y_train,test_size=0.15)\n",
        "X_train.shape,X_val.shape,X_test.shape"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((64617, 446), (11403, 446), (75818, 446))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v4yLi9O_CtFR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "global i\n",
        "i=0\n",
        "def plot_auc(y_true,y_pred,label,dataset=dataset):\n",
        "  \"\"\"\n",
        "  Plots auc curve when y_true and y_pred are given\n",
        "  dataset is string which tells which dataset were used.\n",
        "  label: tells which model was used\n",
        "  If label is a list plots all the auc curve for all labels (here y_pred will be a list of lists )\n",
        "  \"\"\"\n",
        "  \n",
        "  \n",
        "  if (type(label)!=list) & (type(label)!=np.array):\n",
        "    print(\"\\t\\t %s on %s dataset \\t\\t \\n\"%(label,dataset))\n",
        "    fpr,tpr,threshold = metrics.roc_curve(y_true,y_pred) #getting true positive rate, false positive rate\n",
        "    auc = metrics.roc_auc_score(y_true,y_pred) #getting auc score\n",
        "    log_loss = metrics.log_loss(y_true,y_pred) #getting logloss\n",
        "    label_1 = label+' AUC=%.3f'%(auc)\n",
        "\n",
        "    #plotting tpr vs fpr\n",
        "    sns.lineplot(fpr,tpr,label=label_1)\n",
        "    x=np.arange(0,1.1,0.1) #auc=0.5 line \n",
        "    sns.lineplot(x,x,label=\"AUC=0.5\")\n",
        "    plt.title(\"ROC on %s dataset\"%(dataset))\n",
        "    plt.xlabel('False Positive Rate')\n",
        "    plt.ylabel(\"True Positive Rate\")\n",
        "    plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.) #legend box outside graph\n",
        "    plt.show()\n",
        "    print(\"The log loss and auc was found to be %.3f and %.3f for %s on %s dataset.\"%(log_loss,auc,label,dataset))\n",
        "    #creating a dataframe of the result\n",
        "    result_dict = {\"Model\":label,'Dataset':dataset,'log_loss':log_loss,'auc':auc}\n",
        "    \n",
        "    return pd.DataFrame(result_dict,index=[i])\n",
        "\n",
        "  else:\n",
        "    plt.figure(figsize=(12,8))\n",
        "    for k,y in enumerate(y_pred):\n",
        "      fpr,tpr,threshold = metrics.roc_curve(y_true,y)\n",
        "      auc = metrics.roc_auc_score(y_true,y)\n",
        "      label_ = label[k]+' AUC=%.3f'%(auc)\n",
        "      #plotting tpr vs fpr\n",
        "      sns.lineplot(fpr,tpr,label=label_)\n",
        "\n",
        "    x=np.arange(0,1.1,0.1)\n",
        "    sns.lineplot(x,x,label=\"AUC=0.5\")\n",
        "    plt.title(\"Combined ROC\")\n",
        "    plt.xlabel('False Positive Rate')\n",
        "    plt.ylabel(\"True Positive Rate\")\n",
        "    plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.) #legend box outside graph\n",
        "    plt.show()"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "spCK7X7beIiy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def find_best_params(model,params,cv=10,n_jobs=-1,X_train=X_train):\n",
        "  \"\"\"\n",
        "  Funcion which implements Random Seacrh CV and returns best model\n",
        "  \"\"\"\n",
        "  random_cv = model_selection.RandomizedSearchCV(model,param_distributions=params,scoring='roc_auc',n_jobs=n_jobs,cv=cv,verbose=2)\n",
        "  random_cv.fit(X_train,y_train)\n",
        "  print(\"The best auc score was %.3f\"%(random_cv.best_score_))\n",
        "  print(\"The best params were: %s\"%(random_cv.best_params_))\n",
        "  return random_cv.best_estimator_"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Mho8KPmabxm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_feature_importance(df,feat_imp,top=20,size=(6,6)):\n",
        "    \"\"\"\n",
        "    getting most important top features if df and feature_importance array is passed\n",
        "    \"\"\"\n",
        "    top_indices = np.argsort(feat_imp)[::-1][:top] #getting the indices with top feature importace\n",
        "    most_important_feat = df.columns[top_indices] #getiing the columns names\n",
        "    plt.figure(figsize=size)\n",
        "    sns.barplot(x=feat_imp[top_indices],y=most_important_feat) #plotting feature importance \n",
        "    plt.title('Feature Importance')\n",
        "    plt.xlabel('Importance')\n",
        "    plt.ylabel(\"Feature names\")\n",
        "    plt.show()"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VaQtOvkvfNeO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_top_features(df,feat_imp,top=50):\n",
        "  \"\"\"\n",
        "  gets the top features.\n",
        "  if top is of integer dtype it will return top features,\n",
        "  if top is between 0 and 1, it will return features that have at least feature importance value= top\n",
        "  \"\"\"\n",
        "  if (top>0.)&(top<=1.): #getting the features that have feature importance value greater than top\n",
        "    feature_to_consider = [1 if i>=top else 0 for i in feat_imp]\n",
        "    most_important_feat = [i  for i,j in zip(df.columns,feature_to_consider) if (j==1)]#getiing the columns names\n",
        "  else:\n",
        "    top_indices = np.argsort(feat_imp)[::-1][:top] #getting the indices with top feature importace\n",
        "    most_important_feat = df.columns[top_indices] #getiing the columns names\n",
        "  return most_important_feat    "
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vvLQsSsVAsUA",
        "colab_type": "text"
      },
      "source": [
        "# Modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uLh2j2XvAxeO",
        "colab_type": "text"
      },
      "source": [
        "# Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "nsbNtvxAYB2O",
        "colab": {}
      },
      "source": [
        "model = linear_model.LogisticRegression(class_weight='balanced')\n",
        "params = {'penalty':['l2','l1'], 'C':[10.**i for i in np.arange(-3,3,1)], \n",
        "          'fit_intercept':[True,False] ,\n",
        "          }\n"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "mXTZa5oPYB2R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "2a37adbe-8813-4d8b-f150-b6e3314989e2"
      },
      "source": [
        "%%time\n",
        "find_best_params(model,params)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 10 folds for each of 10 candidates, totalling 100 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  37 tasks      | elapsed:  1.3min\n",
            "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  4.4min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The best auc score was 0.812\n",
            "The best params were: {'penalty': 'l2', 'fit_intercept': True, 'C': 0.001}\n",
            "CPU times: user 10.5 s, sys: 1.22 s, total: 11.7 s\n",
            "Wall time: 4min 31s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=0.001, class_weight='balanced', dual=False,\n",
              "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
              "                   max_iter=100, multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "eYp1oCU5YB2U",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "a08cfed7-b00a-446c-da2f-ef7b3a936f4c"
      },
      "source": [
        "model = linear_model.LogisticRegression(C=0.001, class_weight='balanced', dual=False,\n",
        "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
        "                   max_iter=100, multi_class='auto', n_jobs=-1 ,penalty='l2',\n",
        "                  solver='lbfgs', tol=0.0001, verbose=0,\n",
        "                   warm_start=False)\n",
        "\n",
        "model.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=0.001, class_weight='balanced', dual=False,\n",
              "                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,\n",
              "                   max_iter=100, multi_class='auto', n_jobs=-1, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "DlDUHTI-YB2X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "4e5a650a-7b36-46b2-f0f1-0800e5bdf01a"
      },
      "source": [
        "labels=[] #storing all labels\n",
        "y_preds = [] #storing all y_pred\n",
        "y_pred = model.predict_proba(X_val)[:,1]\n",
        "label=\"Logisic Regression\"\n",
        "labels.append(label)\n",
        "y_preds.append(y_pred)\n",
        "result = plot_auc(y_val,y_pred,label,dataset)\n",
        "result_df = result\n",
        "del result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\t\t Logisic Regression on Normal with OHE dataset \t\t \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The log loss and auc was found to be 0.519 and 0.824 for Logisic Regression on Normal with OHE dataset.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hh6_qnWsBOVa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "cd08829a-7e2d-4ea3-b0c9-4b7dbf1f1c18"
      },
      "source": [
        "result_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Dataset</th>\n",
              "      <th>log_loss</th>\n",
              "      <th>auc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logisic Regression</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.518785</td>\n",
              "      <td>0.824362</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Model          Dataset  log_loss       auc\n",
              "0  Logisic Regression  Normal with OHE  0.518785  0.824362"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0y9PvYUNX-t2",
        "colab_type": "text"
      },
      "source": [
        "# Decision Trees"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0kVyDscneGv3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tree.DecisionTreeClassifier(class_weight='balanced')\n",
        "params = { \n",
        "          'max_depth':[10,100,500,1000,-1], \n",
        "         'min_samples_split':[2,5,10,100,500], \n",
        "          'min_samples_leaf':[1,3,5,7,10],\n",
        "          'max_leaf_nodes':[100,500,1000,5000,-1] \n",
        "          }\n"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OWmU9ZdnjHEk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "bc117e44-f556-4936-80ec-f52078415e6b"
      },
      "source": [
        "%%time\n",
        "find_best_params(model,params)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 10 folds for each of 10 candidates, totalling 100 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  37 tasks      | elapsed:  2.1min\n",
            "[Parallel(n_jobs=-1)]: Done 100 out of 100 | elapsed:  6.4min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The best auc score was 0.790\n",
            "The best params were: {'min_samples_split': 500, 'min_samples_leaf': 1, 'max_leaf_nodes': 100, 'max_depth': 1000}\n",
            "CPU times: user 5.95 s, sys: 343 ms, total: 6.29 s\n",
            "Wall time: 6min 30s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight='balanced', criterion='gini',\n",
              "                       max_depth=1000, max_features=None, max_leaf_nodes=100,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=500,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "11n9ZcTNirnF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "290b9994-6728-4769-b261-11d5047279db"
      },
      "source": [
        "model = tree.DecisionTreeClassifier(ccp_alpha=0.0, class_weight='balanced', criterion='gini',\n",
        "                       max_depth=10, max_features=None, max_leaf_nodes=5000,\n",
        "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
        "                       min_samples_leaf=5, min_samples_split=5,\n",
        "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
        "                      splitter='best')\n",
        "\n",
        "model.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight='balanced', criterion='gini',\n",
              "                       max_depth=10, max_features=None, max_leaf_nodes=5000,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=5, min_samples_split=5,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IqO7PiHUhFIR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "23a3a38b-5009-46d1-aba3-f5056764d09c"
      },
      "source": [
        "cc_model = calibration.CalibratedClassifierCV(model,cv='prefit')\n",
        "cc_model.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CalibratedClassifierCV(base_estimator=DecisionTreeClassifier(ccp_alpha=0.0,\n",
              "                                                             class_weight='balanced',\n",
              "                                                             criterion='gini',\n",
              "                                                             max_depth=10,\n",
              "                                                             max_features=None,\n",
              "                                                             max_leaf_nodes=5000,\n",
              "                                                             min_impurity_decrease=0.0,\n",
              "                                                             min_impurity_split=None,\n",
              "                                                             min_samples_leaf=5,\n",
              "                                                             min_samples_split=5,\n",
              "                                                             min_weight_fraction_leaf=0.0,\n",
              "                                                             presort='deprecated',\n",
              "                                                             random_state=None,\n",
              "                                                             splitter='best'),\n",
              "                       cv='prefit', method='sigmoid')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CIxYiOfoih44",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "23795238-559c-4eaf-ea51-d85d001125f1"
      },
      "source": [
        "i+=1\n",
        "y_pred = cc_model.predict_proba(X_val)[:,1]\n",
        "label=\"Decision Tree Classifier\"\n",
        "labels.append(label)\n",
        "y_preds.append(y_pred)\n",
        "result = plot_auc(y_val,y_pred,label)\n",
        "result_df = result_df.append(result)\n",
        "del result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\t\t Decision Tree Classifier on Normal with OHE dataset \t\t \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The log loss and auc was found to be 0.149 and 0.771 for Decision Tree Classifier on Normal with OHE dataset.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qusVRO8HdpHW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "59bef648-88bd-4eb0-cfc9-36a3aaae43b5"
      },
      "source": [
        "result_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Dataset</th>\n",
              "      <th>log_loss</th>\n",
              "      <th>auc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logisic Regression</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.518785</td>\n",
              "      <td>0.824362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.149245</td>\n",
              "      <td>0.771045</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      Model          Dataset  log_loss       auc\n",
              "0        Logisic Regression  Normal with OHE  0.518785  0.824362\n",
              "1  Decision Tree Classifier  Normal with OHE  0.149245  0.771045"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "msPtDUHT0sQN"
      },
      "source": [
        "# Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "OwHc1zRG0sQP",
        "colab": {}
      },
      "source": [
        "model = ensemble.RandomForestClassifier(class_weight='balanced')\n",
        "params = { 'n_estimators':[1000,2000],\n",
        "          'max_depth':[500,1000], \n",
        "         'min_samples_split':[100,500],\n",
        "          'min_samples_leaf':[3,5,7],\n",
        "          'max_leaf_nodes':[100,250] \n",
        "          }\n"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zDOPEuJy0sQS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "d8453231-2fed-4565-b598-2b7b13da947f"
      },
      "source": [
        "%%time\n",
        "find_best_params(model,params,cv=3)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed: 71.5min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The best auc score was 0.818\n",
            "The best params were: {'n_estimators': 2000, 'min_samples_split': 500, 'min_samples_leaf': 5, 'max_leaf_nodes': 250, 'max_depth': 500}\n",
            "CPU times: user 7min 6s, sys: 2.29 s, total: 7min 8s\n",
            "Wall time: 1h 18min 38s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight='balanced',\n",
              "                       criterion='gini', max_depth=500, max_features='auto',\n",
              "                       max_leaf_nodes=250, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=5, min_samples_split=500,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=2000,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "RBeRKL7R0sQW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "cea02a7d-c1d4-4ee0-fa67-127febea10ac"
      },
      "source": [
        "model = ensemble.RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight='balanced',\n",
        "                       criterion='gini', max_depth=1000, max_features='auto',\n",
        "                       max_leaf_nodes=250, max_samples=None,\n",
        "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
        "                       min_samples_leaf=3, min_samples_split=500,\n",
        "                       min_weight_fraction_leaf=0.0, n_estimators=1000,\n",
        "                       n_jobs=-1, oob_score=False, random_state=None,\n",
        "                       verbose=0, warm_start=False)\n",
        "\n",
        "%time model.fit(X_train,y_train)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 6min 5s, sys: 2.01 s, total: 6min 7s\n",
            "Wall time: 3min 10s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight='balanced',\n",
              "                       criterion='gini', max_depth=1000, max_features='auto',\n",
              "                       max_leaf_nodes=250, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=3, min_samples_split=500,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=1000,\n",
              "                       n_jobs=-1, oob_score=False, random_state=None, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "oJLF56Ts0sQY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "3ed6b029-a5bd-43b8-ced2-406b5d24cbbf"
      },
      "source": [
        "cc_model = calibration.CalibratedClassifierCV(model,cv='prefit')\n",
        "cc_model.fit(X_train,y_train)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CalibratedClassifierCV(base_estimator=RandomForestClassifier(bootstrap=True,\n",
              "                                                             ccp_alpha=0.0,\n",
              "                                                             class_weight='balanced',\n",
              "                                                             criterion='gini',\n",
              "                                                             max_depth=1000,\n",
              "                                                             max_features='auto',\n",
              "                                                             max_leaf_nodes=250,\n",
              "                                                             max_samples=None,\n",
              "                                                             min_impurity_decrease=0.0,\n",
              "                                                             min_impurity_split=None,\n",
              "                                                             min_samples_leaf=3,\n",
              "                                                             min_samples_split=500,\n",
              "                                                             min_weight_fraction_leaf=0.0,\n",
              "                                                             n_estimators=1000,\n",
              "                                                             n_jobs=-1,\n",
              "                                                             oob_score=False,\n",
              "                                                             random_state=None,\n",
              "                                                             verbose=0,\n",
              "                                                             warm_start=False),\n",
              "                       cv='prefit', method='sigmoid')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "4Jct69qq0sQa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "40b1b443-74fc-4cbe-8892-80a177b330cc"
      },
      "source": [
        "i+=1\n",
        "y_pred = cc_model.predict_proba(X_val)[:,1]\n",
        "label=\"Random Forest Classifier\"\n",
        "labels.append(label)\n",
        "y_preds.append(y_pred)\n",
        "result = plot_auc(y_val,y_pred,label,dataset)\n",
        "result_df = result_df.append(result)\n",
        "del result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\t\t Random Forest Classifier on Normal with OHE dataset \t\t \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The log loss and auc was found to be 0.137 and 0.833 for Random Forest Classifier on Normal with OHE dataset.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "0igsmQs_0sQd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "2acdf545-cd6d-4b30-d92f-0cffe7026cc6"
      },
      "source": [
        "result_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Dataset</th>\n",
              "      <th>log_loss</th>\n",
              "      <th>auc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logisic Regression</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.518785</td>\n",
              "      <td>0.824362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.149245</td>\n",
              "      <td>0.771045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.137304</td>\n",
              "      <td>0.832997</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      Model          Dataset  log_loss       auc\n",
              "0        Logisic Regression  Normal with OHE  0.518785  0.824362\n",
              "1  Decision Tree Classifier  Normal with OHE  0.149245  0.771045\n",
              "2  Random Forest Classifier  Normal with OHE  0.137304  0.832997"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GZqVnGrga8Tu",
        "colab_type": "text"
      },
      "source": [
        "# Selecting top features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tFB132azbBQS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c6ee1003-f16c-4fd3-8ecb-c4dbfca6a3e5"
      },
      "source": [
        "plot_feature_importance(X_train,model.feature_importances_,top=50,size=(7,12))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BbSVtCcwiTVXobjOByaSpBM9JKo+2XgdcJGkL0mjwsmbxvFupM0lLc58H500vAp8s7LIB8FJXBW9mZtZeTnDNukFErAc0SpoFHAusDWwEvCppUUR8Ati7yuEPAgdHxKCIWAU4sNB2PzApIgZHxGDS9IP7WgnnStLI7RTSiG/ZGsC8/PowoGqd3IhYPz9MRkSsBPw7MDc33w1sHRGb5vfTgBtaicnMzKzbOME16x6jgEfzdITfAmcCpwLbR0QDcBnwQJVjLyGNij5HSnYfb9b2NOnBs6fy60tbCkTSi8CzwA7ALYWmY4BZEfEkKfn+awvdBPDriHg6n/NjpIfmkPQOaR7w7RHxPLA6cG5LMZmZmXWnAU1NTbWOwcxWYKVSaQQwr66ujqFDqw4im60Q+nKp3lKp1KdLJjv+2itfw+LFi2loaADYsL6+fn5H+vIIrplZJ5VKpVqH0Cl9PX7o+9dQjr+vJrdmvY3/Jpn1AxFxMTC+2eZlkraqRTwrmr4+atLX44fOXUNfHjU1s8r8N9qsH5DU55fseuPq6QxasqjWYdgK6KNf+1atQzCzLuYpCmZmZmbWr3gE18w6LZfv/RmphHATcJak6bWNyszMVlQewTWzrnAQsAmphO92wKkRMaKmEZmZ2QrLI7hm/VxENAGnkQpLrAKcJOnm3LYdcA4wLO9+vKR7I+Jc4PPAEOAN4LBCBbRKvgpcKul94PWImAXsm/s2MzPrUR7BNVsxNEoaA+wFXBIR60TEh4H/Bk6QNJpUrrdcVOIsSVvn7dcBZ7fS/wZAMQF+EVi/S6/AzMysjTyCa7ZiuAxAknLlsvFAI/CspP/LbY3Am3n/3SLi68Bq+N8JMzPrYzyCa2YfEBGfBM4DDpBUBxwGrNzKYS8Cnyy83wB4qXsiNDMza5kTXLMVw6EAEbEpMBZ4DHgUGJnn4RIRAyNiTWA4sARYEBErAW1ZY/dG4IiIWCki1ga+BNzU9ZdhZmbWOie4ZiuGQRHxFHA7MFXSa5L+BnwF+HFEPA2UgHpJc0kJ67PAb4B5bej/KuAF4A+k5Pk0SW05zszMrMt5bp3ZiuFcSac235jn325XYfvRwNGFTd9tqfM8f/drnQnwIxOnMHTo0M50YdYhLtVr1v94BNfMrJNKpVKtQ+iUvh4/dO4anNya9T/+W23Wz0ka0BX9RMQYYEaFpgtW9Kpl9fX1tQ6hU2oZf9OypQwYNLhm5zez/skJrpm1iaTZwJju6v+1q85h0JL3uqt766U+dtQZtQ7BzPohT1EwMzMzs37FI7hm1mkR8UXgDGAUcL6k42ockpmZrcCc4JpZV3gBmALsQ+tFIczMzLqVE1yzfi4imoDTgL2BVYCTJN2c27YDzgGG5d2Pl3RvRJwLfB4YArwBHCbpT9XOIen53N+Xuu1CzMzM2shzcM1WDI2SxgB7AZdExDoR8WHgv4ETJI0GxgGP5/3PkrR13n4dcHZNojYzM+sAj+CarRguA5CkiHgSGA80As/mYg/lYg1v5v13i4ivA6vhfyfMzKyP8QiumX1ARHwSOA84QFIdcBieV2tmZn2IE1yzFcOhABGxKTAWeAx4FBiZ5+ESEQMjYk1gOLAEWBARKwHTahOymZlZxzjBNVsxDIqIp4DbgamSXpP0N+ArwI8j4mmgBNRLmgvcCDwL/AaY11rnEfGZiHgZ+CYwNSJejohdu+tizMzMWuK5dWYrhnMlndp8Y55/u12F7UcDRxc2fbelziU9AqzXmQDXOfh4hg4d2pkurA9yqV4z6w4ewTUz66RSqVTrEDqllvE7uTWz7uARXLN+TtKArugnIsYAMyo0XSBpeleco6+qr6+vdQid0tPxNy1bwoBBQ3r0nGa2YnGCa2ZtImk2MKa7+n915jEMXPx2d3Vvvcj6/3lNrUMws37OUxTMzMzMrF/xCK71ShFxKrCapOMqtE0G9pC0T0/HVU0x3oiYBqwi6bwaxjMCeB5oKGzeWdJfO9hfEzBM0rsRcQxwraTXctsEUqWzJmAwMAs4WVJTJy7BzMysw5zgmnUxSRfX8vwRUf57vTCX5+1qxwD3A6/l948A4yQ1RsRg4Nek5cVu64Zzm5mZtcoJrvWIiFgVmAlsASwFBHwDuI5UWGBl4A5JJ1Q4dghwPrAT8AbwVKFtIGn0cELedDdwYi47WymOEcATwKX5mFWAg0jFDLYF/gHsLWlB3v9E4N9Jf1f+DBwhaUFErE4qf1sHLABeAv6SjzmV5aO57Y1vOjBX0k/z+zpSorgxcABp6a7y0znHSXog7zcfuD7fo7nA6ZX6b0lxlLbS+7zt28DHgZsiYhFwoKRnC92snON7v73nNzMz6yqeg2s9ZVdguKSRkkYDU4GFwJ6S6kkPL22VP+5ubiqwITAS2BnYptB2ZD52XP4am7e1ZC3gEUljSUnqA8CFkrYkFTv4D4CImEhKLMdLGgfcCfwo93EK8LakzYB9gM9XOVd745sBTCq8PxSYkT/uvyfHMhbYn/QLQ9FwSdtIOrz8PiKeiIhSRBwfEZ1eTUHSD4BXgH0kjSkntxGxVS4W8RrwIHBHZ89lZmbWUU5wrafMATaPiAsjYl9gMTAQOCci5pASyzoqP6W/IzBT0lJJ7wFXF9p2ISWASyQtAa7I21ryrqRyAvYk8HJeIYAcxyb59V65rycjYjbwdWBEIabLACS9AdxS5Vztii8XTBgWEaPyVIMDWJ7IbgzcExHPAL8A1o2IdQuHX1l4/SqwnqStgN1Io9CH000kPZF/QVgfqAc+213nMjMza40TXOsRkl4gTU+4j5TgzSGVdV0T2DYnR7NIH3F3t8WF143Aombvy1N3BgDfzyOVYyTVSdq+B+KbCUwmJabPSfpT3n4dcJGkLUijwcv44P3651QCSYvLD4HlP68BWou9kfxvQkR06PuQk/27gH07cryZmVlXcIJrPSIi1gMaJc0CjgXWBjYCXpW0KCI+Aexd5fAHgYMjYlBErAIcWGi7H5gUEYPzA06TSEl0V7gNOCoi1szXMDQiRhdiOjRvXwv4cpU+OhLflaSR2ymkEd+yNYB5+fVhQNW6thGxTj5fef7zXsDsavtnzwNb59cHtrDf28DqhXN9KiLKifGHSIn53FbOZWZm1m38kJn1lFHAWREBaWrCmaSHom6MiAbgZdJc2EouAbYEniM9ZPY48NFC2yYsf/DsHtIDZJ0m6aqI+AjwcI57JeAi0ujz6cDlEfE70kNmv2oh9nbFJ+nFiHgW2IGU6JYdA8yKiDdJD6u1tOTXZ4DTIqKRtHTX7cAFLZ2XNKL+84h4C7ihhf1+BlwREe+REuF/AyZHxDLS9/a/gRW6spmZmdXWgKYmL1VpZrVTKpVGAPPq6uoYOrTqoLT1I91RqrdUKvXpksl9PX7o+9fg+GuvfA2LFy+moaEBYMP6+vr5HenLUxTMzDqpVCrVOoRO6en4uzq5NTNrzlMUrF+KiIuB8c02L8urCtRcreKLiCnkZdCamVxYScLMzKxPc4Jr/ZKkabWOoSW1ik/SdDw/tsv19Y8Fezr+95ctYSWP4ppZN3KCa2a9wh+vOoKVFr9V6zCsB2z29VtrHYKZ9XNOcK1HFMvXVmibDOwhaZ+ejquaZuV2pwGrSDqvhvGMIC3j1VDYvLOkqisp5PK9e0hqqLZPF8X2KeDnwMdIa/M+Dhwl6R/deV4zM7NqnOCatULSxbU8f65oBrBQUqVKb7W2BPimpKfyerjXAceRllIzMzPrcU5wrUNy8YCZpOpkSwEB3yAlN8NJFbbukHRChWOHAOcDO5HWtX2q0DYQOBuYkDfdDZwoqbFKHCOAJ0hry04AVgEOAqYB2wL/APaWtCDvfyKpbO0g4M/AEZIWRMTqpNK7daR1bV8C/pKPOZXlo7ntjW86MFfST/P7OlIBiY1Ja9weDZQnIx4n6YG833zSOsE7kYomdDRZ3C8iLiWNrp4r6YLc/7nA5/O53wAOK1dMi4g9gFNJ6+e+D0yS9HREbAucRfr+Apwi6Q5J84H5AJLej4jfApt3MF4zM7NO8zJh1lG7AsMljZQ0GpgKLAT2lFQPjAG2iogJFY6dCmwIjAR2BrYptB2Zjx2Xv8bmbS1ZC3hE0lhSkvoAcGEu/1sirxoQERNJieV4SeOAO4Ef5T5OAd6WtBmwDyn5q6S98c0gVS8rOxSYIamJVPRhfI57f9IvDEXDJW0j6fDy+4h4IiJKEXF8RAxo4bxlq0rajlQ04qyIWC1vP0vS1vl7dx0paS9PN5gOHJDbxgPzImIN4GLgwPz93YNUFGKN4slypbnDSEm8mZlZTTjBtY6aA2weERdGxL7AYlIVq3MiYg4psawjJYPN7QjMlLRU0nvA1YW2XUgJ4BJJS0ilandpJZZ3Jd2RXz8JvFxY8qpEqiQGqVztLsCTETEb+DowohDTZQCS3gBuqXKudsUn6RFgWESMylMNDmB5IrsxcE9EPAP8Alg3ItYtHH5l4fWrwHp5GbHdSKPQh9O663Mc84E3gfXy9t0i4rFcRe44ln+fvgDcKekP+bjFkt4BPk36peSufO/uAppYfm/LUymuBx6U5ATXzMxqxlMUrEMkvRARW5BGYHcDziAlZGsC20paFBGXkKYqdLfFhdeNwKJm78s/5wOA70u6vAdiKpoJTAYeAp4rTwUgjZx+S9KsPHf1PT54v94tv5C0GHgtv34tIq4Btqf1Jb/+5V5ExCeB84CtJc2LiE8D17bSzwDgaUmfq9SYp25cQ0qiv9FKX2ZmZt3KI7jWIRGxHtAoaRZwLLA2sBHwak5uPwHsXeXwB4GDI2JQ/kj7wELb/cCkiBgcEYNJH+/f10Vh3wYcFRFr5msYGhGjCzEdmrevBXy5Sh8die9K0sjtFNKIb9kawLz8+jCgap3aiFgnn688/3kvoKOFGYaTHgxbkBPr4pq89wK7R8Sm+VxDI2IY8H/AphGxYyGmrSNiQO5jBimBPjxPvzAzM6sZJ7jWUaOAR/N0hN8CZ5IeTNo+f+xdngtbySXAi8BzpMTy8WZtT5MePHsqv760KwKWdBVplPHhiHiaNH1h+9x8OrBmRPwOuBn4VQuxtys+SS8Cz5LmwRanPhwDzIqIJ0m/HFRd8gv4DPBUvt9P5HNf0NJ5W4hnLnBjjuk3LE+yyVMTjgB+kc/1KDBC0pukpPq7ETEnIp4jfb8HkEbwJ5J+JkoRMTsiLuxIbGZmZl1hQFOTB1vMrHZKpdIIYF5dXR1Dh1YdxLZ+pDsqmZVKpT5dUa6vxw99/xocf+2Vr2Hx4sU0NDQAbFhfXz+/I315BNfMrJNKpVKtQ+iUno7fZXrNrLv5ITPrEyLiYtKSVUXL8qoCNVer+CJiCnkZtGYmF1aSMDMzW6E4wbU+QdK01veqnVrFJ2k6ra+kYN2sr38s2F3xd8dUBDOztnCCa2a9QsM1UxiwZGGtw7AuNG7aL2sdgpmtoJzgWq9ULI9boW0ysIekfXo6rmqalfOdBqwi6bwaxjMCeB5oKGzeWVJLKzW01F8TMEzSuxFxDHCtpNdy2xjgQlJVtzt70/fFzMxWTE5wzbqYpItref5cUQxgoaRKleQ66xjSesCv5fevAd8kVUP7Qjecz8zMrF2c4FqPyMUJZgJbAEsBkSpeXUcqPLAycIekEyocOwQ4H9gJeIO0Bmy5bSBwNjAhb7obOFFSY5U4RpDWkb00H7MKcBCp2MG2wD+AvSUtyPufSCqLOwj4M3CEpAURsTpprd86YAHwEvCXfMypLB/NbW9804G5kn6a39eRClRsTCoWcTRQntR4nKQH8n7zSWVydwLmktb1bZfiKG2l93nbt4GPAzdFxCLgQEnPAq9ExObtPaeZmVl38DJh1lN2BYZLGilpNDAVWAjsKameNPq3VURMqHDsVGBDYCSpNPA2hbYj87Hj8tfYvK0lawGPSBrL8oIUF0raklT84T8AImIiKbEcL2kccCfwo9zHKcDbkjYD9gE+X+Vc7Y1vBqk6WtmhwIxcHeyeHMtYYH/SLwxFwyVtI+nw8vuIeCIiShFxfEQMaOG8bSLpB8ArwD6SxuTk1szMrFdxgms9ZQ6weURcGBH7AouBgcA5uWJWiTQaWukj9R2BmZKWSnoPuLrQtgspAVwiaSyRbZAAACAASURBVAmpFO4urcTyrqQ78usngZcLS2qVgE3y671yX09GxGzg68CIQkyXAUh6gw9WKCtqV3ySHgGGRcSoPNXgAJYnshsD90TEM8AvgHUjYt3C4VcWXr8KrJeXKduNNAp9OGZmZisAJ7jWIyS9QJqecB8pwZtDmre5JrBtHj2dRZqq0N0WF143AouavS9P3RkAfD+PVI6RVCdpe7rfTGAyKTF9TtKf8vbrgIskbUEaDV7GB+/XP6cSSFpcfggs/3kNy8sSV9NI/jchInri+2BmZtYtnOBaj4iI9YBGSbOAY4G1gY2AVyUtiohPAHtXOfxB4OCIGBQRqwAHFtruByZFxOCIGEz6eP++Lgr7NuCoiFgzX8PQiBhdiOnQvH0t4MtV+uhIfFeSRm6nkEZ8y9YA5uXXhwFV69pGxDr5fOX5z3sBrRV+eB7YOr8+sIX93gZWb6UvMzOzmvFDZtZTRgFnRQSkqQlnkh6KujEiGoCXSXNhK7kE2BJ4jvSQ2ePARwttm7D8wbN7SA+QdZqkqyLiI8DDOe6VgItIo8+nA5dHxO9ID5n9qoXY2xWfpBcj4llgB1KiW3YMMCsi3iQ9rNbSkl+fAU6LiEZgMHA7cEFL5yWNqP88It4Cbmhhv58BV0TEe6RE+D3gEWBVYOWIeBn4rqTLWjmfmZlZtxjQ1NRU6xjMbAVWKpVGAPPq6uoYOrTqoLT1QT1ZyaxUKvXpinJ9PX7o+9fg+GuvfA2LFy+moaEBYMP6+vr5HenLUxTMzDqpVCrVOoRO6a74XabXzGrFUxSsX4qIi4HxzTYvy6sK1Fyt4ouIKeRl0JqZXFhJwszMrE9zgmv9kqRptY6hJbWKT9J0YHotzt2f9fWPBbsq/p6ckmBm1hInuGbWK5SuO5ymJQtrHYZ1wvZH3l7rEMzMAM/BNbM2yoUnzMzMej3/h2XWi0VEE/Bt0jq7awHHS7o5t00gLbc2EHgdmCrp+Sr9rAPcW9i0Aak63LERsS1wFjA8t50i6Y6IGAE8QSofvBNwSUTcD/yctI7xMuAkSXfntXZnkop5LAUkab+uuQtmZmbt4xFcs97vbUlbAweT1qAtJ6xXAQflKnDXkqqVVSTptXJFNlKBioXABRGxBnAxcKCkemAP0lq4a+RD1wIelzRO0sX5HNfmc04Ero6ItYFdgeGSRkoaDUzt6ptgZmbWVk5wzXq/6/OfjwEfz2V0twXmSHo2t10BjImIYS11FBHrAzeREuM/Ap8GNgTuiojZwF1AE6k4BaQyxjfkY4cBY/K5yOeeTVoNYg6weURcGBH78sFyyGZmZj3KUxTMer9FAJIac0W1Dv29jYjhpIpmJ0p6NG8eADwt6XMV9h8B/F1Sq9VgJL0QEVsAOwO7AWdExChJizoSq5mZWWd4BNesb3oMGB0Rm+X3k4CnJL1Taef8gNhNwFWSbio0/R+waUTsWNh364gY0LyP3PfsfC4iYnNgNPBYRKwHNEqaBRxLmqP74U5eo5mZWYd4BNesD5L0ekQcDFybk9fXSXNiq9meNLq6TkSU97tG0jkRsRdwTkT8BBgCvADsWaWfg0hzdI8lPWR2cI5lN+CsPMI8EDhT0iudvEwzM7MOcYJr1otJGlDtvaS7gbvb2M/DpMSzUtvjwA4VmuYDH2m27/OkRLl5H3eR5u+amZnVnBNcM+sV6g+4jKFDh9Y6DOsEVzIzs97CCa5ZPxIRuwNnVGg6SdKdPR3PiqJUKvXpcr1dFb+TWzPrLZzgmvUjOYl1ImtmZis0r6JgZtZJfXn0Fjoff+OyJV0UiZlZ1/AIrpn1Cr/+xaG8v2RhrcOwDthlij80MLPexQmuWS8XEacCq0k6rkLbZGAPSfv0dFyFGEYAzwMNhc07S/prbSIyM7MVnRNcM+uwvAYvwEJJY2oajJmZWeYE16yHRcSqwExgC2ApIOAbwHXAcGBl4A5JJ1Q4dghwPrAT8AbwVKFtIHA2MCFvuptUlrexShzTgbmSfprf1wG3ARsDBwBHkwo/ABwn6YG833zg+hzDXOD09t8FMzOz7uOHzMx63q7AcEkjJY0GpgILgT0l1QNjgK0iYkKFY6cCGwIjSQUXtim0HZmPHZe/xuZt1cwgl93NDgVmSGoC7gHGSxoL7E9KyIuGS9pG0uHl9xHxRESUIuL4SqV+zczMeooTXLOeNwfYPCIujIh9gcWkKmPnRMQcoATUkZLV5nYEZkpaKuk94OpC2y6kBHWJpCXAFXlbRZIeAYZFxKg81eAAlieyGwP3RMQzwC+AdSNi3cLhVxZevwqsJ2krYDfg34HDMTMzqxEnuGY9TNILpOkJ95ES0DnAN4E1gW0lbQnMIk1V6G4zgcmkxPQ5SX/K268DLpK0BWk0eFmzeN4tv5C0WNJr+fVrwDXA9t0fupmZWWVOcM16WESsBzRKmgUcC6wNbAS8KmlRRHwC2LvK4Q8CB0fEoIhYBTiw0HY/MCkiBkfEYNL0g/taCedK0sjtFNKIb9kawLz8+jCgag3diFgnn688v3gvYHYr5zUzM+s2fsjMrOeNAs6KCEhTE84kPbR1Y0Q0AC8DD1Q59hJgS+A50kNmjwMfLbRtwvIHz+4BLm0pEEkvRsSzwA6kRLfsGGBWRLxJelitpSW/PgOcFhGNwGDgduCCls5rZmbWnZzgmvUwSXcBd1Vo2qbCNiSdWni9hCoPjuXVEr6Vv9oTz7/M05V0FXBVYdNJhbYRzfa9BbilPeesZPuvXsHQoVUHiq0Xa1y2hIGDhrS+o5lZD/EUBTOzTiqVSrUOoVM6G7+TWzPrbTyCa9bPRcTFwPhmm5flVQ/MzMz6HSe4Zv2cpGm1jqG/q6+vr3UIndKZ+D09wcx6Iye4ZtaqiFgJ+DWwat70KjBN0vzcfhhpRYhG0pJix0r63/ac48EbJtO4dGGXxWw9Y4/DKk0nNzOrLc/BNbMWRcQgSe8DEySNztXX7gJ+nNvXAn4C7CJpDHAa8POaBWxmZis8j+Ca9XMRcTKwlqRj8/u1AJHWyT2ZVMBhEPADSdfnfR4irWU7HvgbsLuktwrdDgfez68H5K9hwF9Ia+i+3L1XZWZmVp1HcM36vyuB/XM5XkjFIW4D/g/4jKSxpIpq50bEmoXjNsrtu5c3RMSdEbEA+CrwDQBJbwBTgScj4kXgDOCobr4mMzOzqpzgmvVzkl4EngHKiepkYAapgtpNubjEPcCHgSgceq2kZc362h34OKmU78kAETEc+A9ga0kbkMoO/3dEDOimSzIzM2uRE1yzFcMMUhnfUcDqwP8C/z/wEDAqz519mTRdoezdSh3l+biXAQfnTV8EFkpSbr8B2Bj4SJdfhZmZWRs4wTVbMdwCfI5U5WyGpCbSXNn5kpoi4gukMr8VRcTaEVFMWPcF5ubX84BxEbFO3ndH4G1SKWEzM7Me54fMzFYAkt6LiFuBQ4EN8+b/Ai6KiO8BjwNPt9DFusCMiBhMeqBsHjAx912KiB8CD0fEEmAxsE9Oos3MzHqcE1yzFYSkKcCUwvv7gE2r7LtDs/dzgarVACT9mLxsmJmZWa05wTWzXmGn/WYwdOjQWodh7eRKZmbWG3kOrplZJ5VKpVqH0Cmdid/JrZn1Rk5wzczMzKxfcYJrZtZJ9fVVpyf3Ce2Jv3HZkm6MxMysa3gOrpn1CnfdOIllSxfWOgxrxT6H3l3rEMzMWuUE13qliDgVWE3ScRXaJgN7SNqnp+OqphhvREwDVpF0Xg3jGQE8DzQUNu8s6a8d7K8JGCbp3Yg4hlTl7LXcNgE4G2gCBgOzgJO9TJiZmdWKE1yrKiIGSmqsdRx9jaSLa3n+iCj/vV6YK5R1tWOA+4HX8vtHgHGSGvM6ub8GfgPc1g3nNjMza5UT3HaIiGuAAIaSRscOA24Ezpd0a95nD+BbknaMiJHAFcCHgNmkSlHfl3R7lf53AH5CSg62I42I7S/pudx+IsvLoz4O/KekiuVUI2J34IzCppHAvpJujYhJwFGk7/9bwNckKY+MTgTeIa2POjEi1gXOBAYCrwNTJT0fEUEq/7pqbpsh6dwqsawKzAS2AJYCkrRf7vs6YDipROwdkk6ocPwQ4HxgJ1J1rKcKbQNJo4cT8qa7gROrJeZ5ZPMJ4NJ8zCrAQcA0YFvgH8Dekhbk/U8E/j3fqz8DR0haEBGrk8rV1gELgJeAv+RjTmX5aG5745sOzJX00/y+jpQobgwcABwNlB9bP07SA3m/+cD1+R7NBU6v1H9LiqO0ld7nbd8GPg7cFBGLgAMlPVvoZuUc3/vtPb+ZmVlX8UNm7XO0pK0kjQKeAU4kJXmTCvscSkpqAa4iJb91pMR16zacYwvgYklbAjcAJwNExG6k5PbTwChSUvmdap1IulPSmDyCdyEpqbsnIj4L7Ad8TlI9cA5weeHQ8aTEqQ54JV/DQTmea4Fr8n5HAbdJGp33vayFa9oVGC5ppKTRwNS8fSGwZ45jDLBV/ri7uamk6lsjgZ2BbQptR+Zjx+WvsXlbS9YCHpE0Nsf9AHBhvsYS8B8AETGRlFiOlzQOuBP4Ue7jFOBtSZsB+wCfr3Ku9sY3g3/9eSqX1r0nxzIW2J/0S0PRcEnbSDq8/D4inoiIUkQcHxEDWjhvm0j6AennYp/88/UsQERsFRFPk0Z1HwTu6Oy5zMzMOsoJbvsckpOFucCBpMTlFuCzEbFWRKxFSnRujojhpNG9awEkPUHLpVDLJKk8QvkYKcEC2AW4XtLbOdm5JG9rUUTsCnwT2EvSImBPYDTwm4iYDZwFrF845BFJf8yvtwXmFEborgDGRMQw4FfAlIg4PSJ2IiWr1cwBNo+ICyNiX1IpV0hJ+jkRMYeUWNaR7mlzOwIzJS2V9B5wdaFtF1ICuETSkhxja/flXUnlBOxJ4GVJs/P7EmmkHWCv3NeT+V59HRhRiOkyAElvkH4OKmlXfJIeAYZFxKg81eAAlieyG5N+SXkG+AWwbh4FL7uy8PpVYD1JWwG7kUahD6ebSHoi/4KwPqni2We761xmZmatcYLbRnnk82vAhDyCezKwck64biUlvAcCt0r6e+HQ9j5os6jwupFOTCOJiNHAxaSP3N/ImwcAl5dHd/MI7AaFwypOeWhO0s2kJOaPwH+RRnqr7fsCaWT6PlJyNyciViYl3msC2+bkaBbpI+7utrjwupHq93wAaUpJ+V7VSdq+B+KbCUwmJabPSfpT3n4dcJGkLUijwcv44P365/dO0uLyQ2D5z2uA1mJvJP+bkL8/7ZZ/zu4C9u3I8WZmZl3BCW7brUGar/rXiBhKmn9bNoOUkEwmT0+Q9DZpGsMBABExjjS1oKPuB74aEcPyR81TSAljRRHxCeBmYKKk3xeafkkaiV4v7zcwIqotgvkYMDoiNsvvJwFPSXonIjYBFkiaAXyPD04baB7LekCjpFnAscDawIdJ9/RVSYtyvHtX6eJB4OCIGBQRq5B+kSi7H5gUEYPzA06TaOG+tNNtwFERsWa+jqH5l4ZyTIfm7WsBX67SR0fiu5L0czOF5dNdIN2vefn1YaS54BVFxDr5fOU50HuR5oG35HmWT6M5sIX93gZWL5zrUxFRTow/RErM57ZyLjMzs27jBLft7iaNVv4eeJj00Tbwz4+Vh5PmQD5SOOYQ4Jg8peE40n/6b3Xk5JLuIn00/yjLk4fvt3DIFFIieWFEzM5fO0r6FfBt4LY8NaCBKomlpNdJ836vzfMrJ+YvSPN450bEU6QHwI5uIZZRwKP5fL8FzpT0CvAzYPuIaGD5XNhKLgFeBJ4jJZaPN2t7mvTg2VP59aUtxNJmkq4ijXw+nK+/xPJR0NOBNSPid6RfJH7VQuztik/Si8CzwA58cOrDMcCsiHgS2AhoacmvzwBP5Xv+RD73BS2dlzSi/vOIKJF+dqr5GXBF/pkaSfr5mZvP9RvSvZjeyrnMzMy6zYCmJi9V2V0iYjXg75KaciLwEBCS3qxtZGa9R6lUGgHMq6urY+jQqoPS1ks0LlvCwEFDWt+xh5VKpT5dUa6vxw99/xocf+2Vr2Hx4sU0NDQAbFhfXz+/I315BLd7fRqYnUf/rictMeXk1qyfKZVKtQ6hU9oTf29Mbs3MmvM6uN1I0r3Avc23R8RtwAbNNr8oaa/2niMiTgG+UqHpi+WHjHpKL4vlYtKSZ0XL8qoCNVer+CJiCnkZtGYmF1aSMDMz69Oc4NZARxLZFvo6DTitq/rrjF4Wy7Rax9CSWsUnaTqeH9vl+vrHgm2Jv7dOTTAzq8QJrpn1CrNuOoSlS1taTtlqaeLke2odgplZm3kOrplVFBGnRkS18suTI+Kmno7JzMysLZzgWo+LiIG1jqGv8L0yMzNrP09R6AUi4hogSAv3P09axP9G4HxJt+Z99gC+JWnHvOTYFcCHSIv3b0KquHV7lf53AH5CWqN0O1J1tf0lPZfbTyStdwtpjdn/lFSxollE7A6cUdg0EthX0q0RMQk4ivRz9RbwNUmKiMmk9XPfATYFJuYSs2eSyvW+DkyV9HxEBKlwxqq5bYakiqOIOZ4m0rq+XwbWAo7PVdaIiAmVztFCXw/l698O+Dhwg6T/ym2bAD8nrQ+7DDhJ0t0txdCb7lUu9jCTVFFuKakk9H657+tI6zivDNwh6YQKxw8hrXe8E/AGaV3dcttA4GxgQt50N3CipMZq99rMzKw7eQS3dzha0la5BPAzwImkxGVSYZ9DWV7V6ipS8ltHSly3pnVbABfnkrg3kEoNExG7kZLbT5MKMgwEvlOtE0l3lkvXAheSigjck0sZ7wd8TlI9cA5weeHQ8cBxOeZX8jUclOO5llRQAVLSd1suIVxHKgDRmrclbZ2v42f5utZp4Rwt2QD4HDAWmBIRm+bt1wDX5r4mAldHRLEYwr/E0Mvu1a6kQiQjJY0GpubtC4E9cxxjgK3yLwbNTQU2JCXpO/PBynVH5mPH5a+xeZuZmVlNOMHtHQ6JiFKueHYgKVm4BfhsRKyVS8F+Hrg5IoYDdaREB0lPkKpjtUaSyqNujwEb59e7ANdLeltSE6ny1i6tdRYRu5IqX+0laRGwJzAa+E1EzAbOAtYvHPKIpD/m19sCcyQ9m99fAYyJiGGkKlhTIuL0iNiJlIC15vrCdX08IlZu5RwtuVHS+5LeIlVO2zgfM4blZZifJY2cF5f5qhQD0Gvu1Rxg84i4MCL2BRbn7QOBc3IVshLpZ2tMheN3BGZKWirpPVJVvbJdSKPHSyQtyTG2+jNkZmbWXZzg1lgezfsaMCGP4J4MrJyTiFtJCe+BwK2S/l44tL0l6BYVXjfSiekpETEauBjYW9IbefMA4PLyiGUeVSyu9VtxykNzeXrBZ0llkf+LNHrZmkX52PJH4p2ZetPR+1Qxht5yryS9QBrFv4+UfM7JSfg3gTWBbfMI8SzSVAUzM7M+ywlu7a1BmoP514gYSpp/WzYDmJy/yqOHb5OmMRwAEBHjSFMLOup+4KsRMSwiBgBTSElQRRHxCeBmYKKk3xeafkkaiV4v7zcwIqotrvkYMDoiNsvvJwFPSXonz3VdIGkG8D0++FF4e1Q9R3s7ysfMzn0QEZuTRmAfa+m43nSv8rkaJc0CjiXNJf4w6efvVUmLcrx7V+niQeDgiBgUEauQfukqux+YFBGDI2JwjrHqz5CZmVl380NmtXc3aU7n70kP7/yKnKhIeiRPSUDSI4VjDgEuj4j/D5ibv97qyMkl3RURWwKP5k1PAN9v4ZAppOTowvSMEwDHSvqfiPg2cFt+6GgI6UG5f6kBKun1iDgYuDYiBpEenJqYm/cDDoqIJaRR6qM7eF0tnaMjDgJ+HhHHkh4yO1jS660c05vu1SjgrBzHQOBMSa9ExM+AGyOiAXgZeKDK8ZcAW5KmbbxBehjvo4W2TVj+4Nk9wKUtxGJmZtatBjQ1tfeTbqu1iFgN+LukpryiwkNASHqztpGZtV+pVBoBzKurq2Po0KG1Dseq6O2VzEqlUp+uKNfX44e+fw2Ov/bK17B48WIaGhoANqyvr5/fkb48RaFv+jQwOyKeJj3cdISTW7PaKZX+ZfC9T2lL/L05uTUza85TFPogSfcC9zbfHhG3kZa5KnpR0l7tPUdEnAJ8pULTFyW91t7+OqMrY6mwNm3ZSZLu7Eh8vUlv+r6ZmZnVihPcfqQjiWwLfZ0GnNZV/XVGV8aSk9g+n8hW05u+byuSvv6xYLX4lzUuYdBAj9yaWd/jBNfMeoUbbjmEJUvbsuyx9ZTDD7mn1iGYmXWIE1zrERFxKrCapOMqtE0G9pC0T0/HVU0x3oiYBqwi6bwaxjOCVMa5obB5Z0l/beGY+aT72lBtny6KbSXg16SSwQCvAtMkze/O85qZmVXjBNesFZIuruX58/JgAAtz2d9eRdL7ETEhV38jIo4GfkzlucBmZmbdzgmudUhErArMJFXHWgoI+AZwHTCcVA3rDkknVDh2CHA+sBNpTdWnCm0DgbOBCXnT3cCJhQphzfsaQVq799J8zCqkNWunkcrc/oNURWxB3v9E4N9JP/t/Jq1AsSAiVgcuI5WqXQC8BPwlH3Mqy0dz2xvfdGCupJ/m93XAbaRSyQeQ1q4tT3I8TtIDeb/5pBUydiKtc3x6pf7bYL+IuBT4GHCupAty/+eSyj8PIX0PDpP0p9y2B3AqMBh4H5gk6emI2JZUVnh47vsUSXcAlJPbbHg+zszMrCa8TJh11K7AcEkjJY0GpgILgT0l1QNjgK0iYkKFY6cCGwIjgZ35YAWuI/Ox4/LX2LytJWsBj0gaS0pSHwAuzKVnS8B/AETERFJiOV7SONLDZj/KfZwCvC1pM2AfUvJXSXvjm0GugJYdCsyQ1EQqiDA+x70/6ReGouGStpF0ePl9RDwREaWIOD5XnmvNqpK2A3YgFXpYLW8/S9LW+Xt3HSlpJyI+BUwHDsht44F5EbEGqeTwgfn7uwep8MUa5RNFxJ0RsQD4KumXHTMzs5pwgmsdNef/sffm8XaO5/7/Ow1iSkqDn1ZotPgoOxIixlJjDV9DtSgREmnUdFRbWr6qquqUllNOKz0EFVFBDV9RM6dFtagsGQ2fliYnbUkNx0x2iP37476XPNnWWnvea+/s6/167Vee57mn637WTnKt67me6wN8TtJESYcAjSSFrAskzSI5lg0kZ7A5uwJX237P9jvArwtte5AcwMW2F5MkivdowZa3ypFE4AngH7Zn5vMSSWUL4IA81xOSZgInAkMLNl0JYPtl4JYqa7XJvqxAN1DSsJxqcDhLHdnPAvdIehK4AVhX0rqF4VMKxy8AQ2xvDexDikJ/jZa5PtsxH3gVGJKv7yPp0axgdipLP6c9gTtt/zWPa8xSxTuQvpTcle/dXST1tPK9xfa+wKdIDvOZrbAtCIIgCLqESFEI2oXtv0nanBSB3YdUW3YKsCawre1FkiaRUhW6msbC8RJgUbPz8u95P+Bc27/qBpuKXA2MIynOPV1OBSA5gqfYvjW/qPUOy96vt8oHthuBF/Pxi5KuBXYkRVtr8ZF7IenTwEXAKNvzJO0ATG1hnn7AbNs71+qU83GvBP4KnNDCnEEQBEHQJUQEN2gXkoYAS2zfCnwLWBv4DPBCdm7XAw6sMvx3wJGSVpC0CjC60HY/MFbSipJWJD3ev6+TzL4NOEHSmnkPAyQNL9h0dL4+GDioyhztsW8KKXI7gRTxLbMGMC8fjweq6tRKWievV85/PgCYWa1/CwwCFgMLs2N9XKHtXmBfSRvntQZIGgj8CdhY0q4Fm0ZJ6idpbUlrFeY4hJQ3HARBEAR1IRzcoL0MAx7J6Qh/Bs4jvZi0Y37sXc6FrcQkYAHwNMmxfLxZ22zSi2cz8vHlnWGw7WuAa4EHs8xxiRQFhfQS15qSngFuBh6qYXub7LO9AHiKlAdbTH34JnCrpCdIXw6qlvwCPg/MyPd7el77klrr1rBnDnBjtukxljrZ5NSEY4Ab8lqPAEOzFPQBwA8kzZL0NOnz7gesS0q1mC1pDinNYUx7bAuCIAiCzqBfU1NTvW0IgqAPUyqVhgLzGhoaGDCgahA7qAO9ScmsVCr1akW53m4/9P49hP31p7yHxsZG5s6dC7DhyJEj57dnrojgBkEQdJBSqVRvEzpENft7i3MbBEHQnHjJLOgVSLqUVLKqyPu5qkDdqZd9kiaQy6A1Y1yhkkQQBEEQ9CnCwQ16BbaPa7lX/aiXfbavoOVKCkEX09sfC1ayvzelJwRBEDQnHNwgCHoE19x6FI3vvVZvM4LMiWPuqbcJQRAE7SYc3KBbKMrdVmgbB+xn++DutqsazeR5jwNWsX1RHe0ZCjwLzC1c3t121coLWe53P9tzq/XpJNs+BvwRWDVfegE4LotLBEEQBEG3Ew5uH0JSf9tL6m1Hb8P2pfVcPyugAbxmu5IyXF3J4g57234dQNLJwM+AL9fXsiAIgqCv0qcd3KwGJVKB/WdJxfZvBH5he1rusx9JbWpXSZuRCvWvRiqyvxFJGev2KvPvAlxMqjW6PUna9DDbT+f204Ajc/fHgZNsv1VhKiTtS1ILK7MZcIjtaZLGklSjVgBeB4637RwZHQO8CWwMjMlSsOeRZHVfAo61/awkAZNJUbj+JDnaC6vYsipJnWtz4D3Atg/Nc19HEhJYGbjD9ncrjF8J+AWwG/AyqaZrua0/8BNg73zpbuC0ao55jmxOJ9Wi3RtYBTiCJF6wLfAucKDthbn/aSSZ2xWAfwLH2F4o6eOk2r0NwELg78C/8pizWRrNbat9VwBzbP9nPm8gCU58liT+cDJQTnQ81fZ/537zSTK7u5FEE35Uaf5WcKiky4FPAhfaviTPfyHwhbz2y8D4ssJa/p0/G1gR+AAYa3u2pG2B80mfL8BZZYnksnObGZTHBUEQBEFd6Otlwk62vbXtYcCTwGkkJ29soc/RLFWfuobk/DaQHNdRrVhjc+BS21sAvwHOCYIucQAAIABJREFUBJC0D8m53YEkmtAf+H61SWzfaXtEjuBNJDl190jaCTgU2Nn2SOACoChFux3JcWoAns97OCLbM5UkfADJQb7N9vDc98oae9oLGGR7M9vDgWPz9deA/bMdI4CtJe1dYfyxwIYkJ313YJtC29fz2K3yz5b5Wi0GAw/b3pKlAhMT8x5L5CoDksaQHMvtbG8F3An8R57jLOAN25sCB5Ocv0q01b7JfPT3abLtJuCebMuWwGGkLw1FBtnexvbXyueSpksqSfqOpH411i2zqu3tSSIT50taPV8/3/ao/PldR3LakbQJ6aW1w3PbdsA8SWsAlwKj8+e7H3BZvk4ee6ekhcBXgW+0wrYgCIIg6BL6uoN7VHYW5pDkYkeQlKZ2kjQ4S7Z+AbhZ0iBSdG8qgO3pJBWrlrDtcoTyUZKDBbAHcL3tN7KzMylfq4mkvYBvAwfYXgTsDwwHHpM0kxRhW78w5GHbz+XjbYFZtp/K51cBI7IU60PABEk/krQbyVmtxizgc5ImSjoEaMzX+wMXZAWsEul+VXqkvitwte33bL8D/LrQtgfJAVxse3G2saX78lY5kgg8AfyjUCKrRIq0Q1Li2gN4It+rE4GhBZuuBLD9MssqjhVpk322HwYGShqWUw0OZ6kj+1nSl5QngRuAdXMUvMyUwvELwJBcdmwfUhT6a7TM9dmO+cCrwJB8fR9Jj2bVuVNZ+jntCdyZFc2w3Wj7TdIXsQ2Bu/K9u4v0RKJ8b7G9L/ApksN8ZitsC4IgCIIuoc+mKOTI5/HADrZfkjQa+LrtdyRNIzm8ANNsv50dXEj/qbeFRYXjJXTgnksaToqi7ZWdMEhSqb+yfVaVYRVTHppj+2ZJjwBfBE4npWtUlFu1/TdJm5Oir/sAP5Y0jOR4rwlsa3uRpEmkVIWuprFwvITq97wfKaWkGOHuDq4GxgEPAE+XUwFIjuAptm/NL2q9w7L368PPznYj8GI+fjGn1+xIyyXCPnIvJH0auAgYZXuepB3IX9xq0A+YbXvnWp1yPu6VwF9JTwWCIAiCoNvpyxHcNUj5qq9IGkBy6MpMJjkk48jpCbbfIKUxHA4gaStSakF7uR/4qqSB+VHzBOC+ap0lrQfcDIyx/ZdC029JkeghuV9/SdWKcj4KDJe0aT4fC8yw/aakjYCFticDP2TZtIHmtgwBlti+FfgWsDbwCdI9fSE7t+sBB1aZ4nfAkZJWkLQKS79MQLovYyWtKGnFbGPV+9JGbgNOkLRm3seA/KWhbNPR+fpg4KAqc7THvimk35sJLE13gXS/5uXj8aRc8IpIWievV86BPoCUB94eBgGLgYXZsS7W8L0X2FfSxnmtATnC/ydgY0m7FmwaJamfpLUlrVWY4xBS3nAQBEEQ1IW+7ODeDTwH/AV4kPRoG/jwsfIgUg7kw4UxRwHfzCkNp5L+Ey++XNNqbN9FejT/CEudgXNrDJlAciQnSpqZf3a1/RDwPeC2nBowlyqOpe2XSHm/UyXNJkVoy1HaQ4E5kmaQXgA7uYYtw4BH8np/Bs6z/Tzwc2DH/Ni7nAtbiUnAAuBpkmP5eLO22aQXz2bk48tr2NJqbF9Dyjl+MO+/RIqCQnqJa01Jz5C+SDxUw/Y22Wd7AfAUKQ+2mPrwTeBWSU8AnwGqlvwCPg/MyPd8el77klrr1rBnDullyqdIL0DOK7T9FTgGuCGv9Qgw1ParJKf6B5JmSXqa9CJaP2BdUqrF7Px3Y0+qRP+DIAiCoDvo19TU1ifufZf8gs7btpuUKio8ACj/5x8EQTsolUpDgXkNDQ0MGFA1iB10M71NyaxUKvVqRbnebj/0/j2E/fWnvIfGxkbmzp0LsOHIkSPnt2euvhzBbQ87ADNz9O96UompcG6DoI9TKpXqbUKHqGR/b3JugyAImtNnXzJrD7bvJeUoLoOk24ANml1eYPuAtq4h6SwqF8j/ou0X2zpfR+hhtlxKKllV5P1cVaDu1Ms+SRPIZdCaMa5QSSIIgiAI+hTh4HYC7XFka8x1DnBOZ83XEXqYLce13Kt+1Ms+21fQciWFoIvpyY8Fe1uqQRAEQWcQDm4QBD2Cy287ikXv1yq/HLSHUw+/p94mBEEQdDvh4AbdQlHutkLbOGA/2wd3t13VaCbPexywiu2L6mjPUJKc9NzC5d1tV628kOV+97M9t1qfTrJtE+Aykhzw+6SqGCfYfrcr1w2CIAiCaoSD24eQ1N/2knrb0duwfWk9188KaACvZanmnsZi4Nu2Z+S6uteRyuj9qL5mBUEQBH2VPu3gZjUokQrsP0sqtn8j8Avb03Kf/UhqU7vm0mBXAauRiuxvRFLGur3K/LsAF5NqjW5PUkE7zPbTuf00Ul1aSFGvk2xXVB6TtC/w48KlzYBDbE+TNJakGrUCqS7v8badI6NjgDeBjYExWQr2PJKs7kvAsbaflSSSwMWquW2y7Qur2LIqSZ1rc+A9khzxoXnu60g1hFcG7rD93QrjVyLV2t0NeJlU07Xc1h/4CbB3vnQ3cFo1xzxHNqeTatHuDawCHEESL9gWeBc40PbC3P80ksztCsA/SZUwFkr6OKl2bwOwEPg78K885myWRnPbat8VwBzb/5nPG0iCE58liT+cDJQTJE+1/d+533xSpY7dSHWS2+ssHirpclJ09ULbl+T5LyTJUK9E+gzGlxXW8u/82cCKwAfAWNuzJW1LkoIuq/qdZfuOLAM8Hz5UMvsz8Ll22hsEQRAEHaavlwk72fbWtoeRVMpOIzl5Ywt9jmap+tQ1JOe3geS4jmrFGpsDl9reAvgNcCaApH1Izu0OJOGE/sD3q01i+07bI3IEbyLJqbsnSw4fCuxseyRwAVCUot2O5Dg1AM/nPRyR7ZlKEj6A5CDfZnt47ntljT3tRRLB2Mz2cODYfP01YP9sxwhga0l7Vxh/LLAhyUnfnWVV076ex26Vf7bM12oxGHjY9pYsFZiYmPdYIlcZkDSG5FhuZ3sr4E7gP/IcZwFv2N4UOJjk/FWirfZN5qO/T5NtNwH3ZFu2BA4jfWkoMsj2Nra/Vj6XNF1SSdJ3sgJeS6xqe3uSyMT5uZYzwPm2R+XP7zqS015ON7gCODy3bQfMk7QGSSZ6dP589wMuy9c/JCvTjSc58UEQBEFQF/p0BJckcXsEKYq1GknV7BzgoizXCsnROUrSIFJ0byqA7em5Hm5L2HY5QvkosH8+3gO4PksAI2kS8J8tTSZpL+DbwE5ZEnd/YDjwWArC0g9YszDkYdvP5eNtgVm2n8rnVwG/zFKsDwE/zdHZ3+efaswCPidpIkns4o58vT9wgaQdWKpwNYIU5SyyK3C17feA9yT9mqTUVb4vk20vzvu9iiSb+1817HnLdtmGJ4B/FEpklUjKWpCUuLYGnsj3qhzxLtt0EoDtlyUVFceKtMk+2w8ryTEPIym3HU6K5kNytq/LssbvAetKWrccbSZJ/JZ5ARhi+0VJ65AcyFdpuYLC9dmO+ZJeBYYAzwD7SDoRWJ1l/x3YE7gzK5phuxFozE8QNgTuyvcO0hOJjUhftsqpFNcDv7MdDm4QBEFQN/qsg5sjn8cDO9h+SdJo4Ou235E0DRidu06z/XZ2cCH9p94WFhWOl9CBey5pOCmKtpftl/PlfsCvbJ9VZVjFlIfm2L5Z0iPAF4HTSVG4inKrtv8maXNS9HUf4MfZgfs2ybneNjvfk0ipCl1NY+F4CdXveT9SSkkxwt0dXA2MI30ZeLqcCkCKnJ5i+9acu/oOy96vDz+77Gi+mI9fzOk1O9Kyg/uReyHp08BFwCjb8/IXkqktzNMPmG1750qNOXXjWpLT/Y0W5gqCIAiCLqUvpyisQYrevSJpAMmhKzOZ5JCMI6cn5Ejrk6QIHJK2IqUWtJf7ga/m6F4/YAJwX7XOOcp3MzDG9l8KTb8lRZiH5H79JVUryvkoMFzSpvl8LDDD9puSNgIW2p4M/JBl0waa2zIEWGL7VuBbwNrAJ0j39IXs3K4HHFhlit8BR0paIT/SHl1oux8YK2lFSStmG6velzZyG3CCpDXzPgbkLw1lm47O1weTorKVaI99U0i/NxNYmu4C6X7Ny8fjSbngFZG0Tl6vnAN9ACkPvD0MIr0YtjA71sUavvcC+0raOK81IEf4/wRsLGnXgk2jJPXLc0wmOdBfy+kXQRAEQVA3+rKDezfwHCkt4UHSo20gPVYmOQGD8nGZo4BvSppDekt8DksfcbcJ23cBvwYeyfMAnFtjyASSIzlR0sz8s6vth4DvAbdJmkUqI1XRsbT9Einvd2pOrxjD0ijtocAcSTNIL4CdXMOWYcAjeb0/A+fZfh74ObCjpLkszYWtxCRgAemR/e9IL9gV22aTXjybkY8vr2FLq7F9DSnK+GDef4kUBYX0Eteakp4hfZF4qIbtbbLP9gLgKVIebDH14ZvArZKeAD4DVC35RUrhmJHv+fS89iW11q1hzxzSy5RPkV6AnFdo+ytwDHBDXusRYKiTJPUBwA8kzZL0NOlFtH6kKP4Y0u9FKf9uTmyPbUEQBEHQGfRraopgS2vJL+i8bbtJqaLCA4Dyf/5BELSDUqk0FJjX0NDAgAFVg9hBO2mNklmpVOrRamytobfvobfbD71/D2F//SnvobGxkblz5wJsOHLkyPntmasvR3Dbww7AzBz9u55UYiqc2yDo45RKpXqbUJWQ6Q2CoC/SZ18yaw+27yXlKC6DpNuADZpdXmD7gLauIeks4MsVmr5o+8W2ztcRepgtl5JKVhV53/bW3WlHNepln6QJ5DJozRhXqCQRBEEQBH2KcHA7gfY4sjXmOodUqqzu9DBbjmu5V/2ol322r6DlSgpBF9MTHgu+t2QxK0a0NgiCAAgHNwiCHsLFdxzFu++/Vm8zei1nH3pPvU0IgiDoMYSDGwQ9BEkzge1tv9vGcfOB/WzP7Qq7CuvsTVI8ayLJ+N4KnFkuCybp+6TSepDEMNorLxwEQRAEHSJeMguCHkKWYm6Tc9tdZJWyh4Gtslz0CJLq2f65fWfgEJLaXwNwSL4WBEEQBN1ORHCDoIcgqQkYaPutHJWdQnIiPwlcaPuS3G8n4Jd52IOkWrS15v0rcLDtWfn834CRto+WdCFJjnol4GVgvO3/kTSUVG93MrAbMMn2pYVpV85jPsjnXwWmlB10SVPytWr1hIMgCIKgy4gIbhD0XFa1vT1JIOJ8Satn1b3rgZNsDyM5kM0reDTnapLiWpmjWaqodr7tUbaHk6SDf1LoNxh43PZWZedW0ta5TN6LJJGOO3LfDYD/KYxdAKzfpt0GQRAEQScRDm4Q9FyuB7A9H3gVGAIIeMf2A7ntN7SspjcFODxLIw8jSQT/IbftI+nRrD53Kin1oMwi4DfFiWxPt70FyXkdCezU7t0FQRAEQRcRDm4Q9FwWFY6XUD2lqKYcYZYKfpIkqTuO9AJYk6RPAxcBh9tuAMaTUg/KvF1+gazCnC8Dd5HybiFFbD9d6LIB8PdadgVBEARBVxEObhD0LgyskvNwkXQwKSLbEpOBCcDhpJQFgEHAYmChpI8BNWv5Stok90PSaiSHeU5uvhE4StIqklYBjqJZ9DcIgiAIuotwcIOgF2G7keSk/jLnwu5Cip62xC2571M5oovtOSTH9CngMWBeC3McCMyRNCv3f4gsMpFTJm4hRYqfBG6x/WAbthYEQRAEnUZUUQiCHoLtfoXjoc3ahhaO/wAMKzRXkuptPvc7wMcrXD8ZOLlw6Qf5+nxgrWZ9LwAuqLHG2cDZLdlSjW/+nykMGDCgvcP7PKFkFgRBsJSI4AZBEHSQUqlUbxPCuQ2CICgQEdwgWE6QNJ2P/p1+1HbN3NogCIIgWN4IBzcIlhNsb11vG/oqI0eOrOv6kZ4QBEGwLOHgBkHQIzjn7rG8/f5r9TajV3LxV+6utwlBEAQ9isjBDYKgKpJ2yakPldqGSnq5u20KgiAIgpYIBzcIejiS4klLEARBELSB+I8zCCogqQn4HnAQMBj4ju2bc9vewHlAf+Al4Fjbz1aZZx3g3sKlDYCrbX9L0rbA+STBBYCzbN8haSgwnSTOsBswSdL9wGXA2sD7wBm275a0Kkm4YXPgPcC2D62xrx+Q6uguIimg7Wr7NUnXkmSABwDPAuNtv1ph/InAt4A3gDuatR0FfCfP+1y+Ly9WsyUIgiAIuoqI4AZBdd6wPQo4Evg5fOiwXgMcYXsLYCpwbbUJbL9oe4TtEcDRwGvAJZLWAC4FRtseCewHXJavQ3KqH7e9le1L8xpT85pjgF9LWhvYCxhkezPbw4Fjq9ki6RMk53TLbM/OwFu5+WTbW9seRhJqOK3C+C1ITv+OtrfKNpbbGkjO+hezjXOBX1SzJQiCIAi6knBwg6A61+c/HwU+JWllYFtglu2ncttVwAhJA2tNJGl94CaSY/wcsAOwIXCXpJnAXaTI50Z5yCKy1G2ee0Rei7z2TGA7YBbwOUkTJR0CNNYw43VSdHaKpGOA1W2/n9uOklSSNAcYnddrzi7AHbb/lc8nFdp2Be60/UI+vwzYo9Y9CYIgCIKuIhzcIKjOIgDbS/J5u1J6JA0CbgdOs/1IvtwPmF2O7uaf9W2XX+h623ZTS3Pb/hspPeE+kkM5KzvilfouITnFlwBDgJKkLSTtBBwP7J0juGcCFecIgiAIgt5AOLhB0DYeBYZL2jSfjwVm2H6zUuf8gthNwDW2byo0/QnYWNKuhb6jJPVrPkeee2ZeC0mfA4YDj0oaAiyxfSsp/WBt4BNVbBkIrG37Qds/IKURNABrkKK7r0gaAIyvsvcHgH1zmgbA1wptv89t6+bzY0hOdxAEQRB0O/GSWRC0AdsvSToSmJqd15dIObHV2BHYHVhHUrnftbYvkHQAcIGki4GVgL8B+1eZ5whSju63SC+ZHZlt2Qc4XxKkl97Os/18lTk+DtwsaRXSl9sngFuAJXkPfwFeBh4Ctqmw99mSfgz8UdIbwJ2FtrmSTgfuyy/o/Y0a+cBBEARB0JX0a2pq8SloEARBl1EqlYYC8xoaGhgwYEC9zemVdFTJrFQq1V2NraP09j30dvuh9+8h7K8/5T00NjYyd+5cgA1Hjhw5vz1zRYpCEARBBymVSnVdP2R6gyAIliVSFIKgE5C0L/DjCk1n2L6zwvU+YUsQBEEQ1INwcIOgE8iOY49wHnuSLX2FrnwsuHjJYlaKCG0QBEGbCAc3CIIewYn3jeWNJa/V24wex28OvLveJgRBEPQ6wsENaiLpVpIgwQck1auTbM/MbZuQZGIHA68AR9n+axvmHgFMBLYkiQQc3IoxvyIpgg20/VaWtX2WVPKqzO62X8n9jyGpcvUjiSl8w/YHknYhRTn/ksc02t62NXZJ2g34KbBKvjTa9qy81kl5rSbgp7Z/3WysgBnAL22fmq9tQhJNWIMklXuD7bNz2/eAr5IqHfQjVUm4IbfdD6yVp16BVA93eK52UGtc1bbcfhJwIkn6d0lWPau5Xq1xQRAEQdDdxEtmQUVyCSyAsbaH294SuBD4VaHbpcBE25uQHMLL2rjMi8C3SfVbW2PT/iTHsTmvNRNMKDu3GwI/ALYHNs4/xZJeTxXGbNsauyStR7oHR9jeHNgamJeb/wrsksUS9gUuzg54eWx/0j26tdm0PwVuyg7hKOBoSeUyXZfY3iLf/32ByyWtCWB7j4IM8JnAk2Vns9a4Wm2SvgwcAozK+9irbGSt9WqNC4IgCILuJiK4XUyuCfo94CBSpPM7tm/Ojs9022vlfh+el4+By4G9SZHCI4DjSFKx7wIH2l5YZc0xwFdsH5TPVwAWkGqyrg78EliNpFY1yfbFud9kUo1VAQOBEbZfL0z9cVIkl1zsfytgz9x2HXCJpLVtv9Sae5PrtT6fhQtqImkwyVndnepCBM05GLi1bI+ky0nR3ykdsOsEYIpt577vkj4PbD9QmOMfkl4gKYbNz5dPJymarZ5/yjSR7i3Aqvn8xTxP8f6vntsqfTEdT+HLR61xLcx5CvD9snBFQZa35nptGBcEQRAEXU5EcLuHN2yPAo4Eft7KMYOBh3OU7Urgv0nR0i2AEvBvNcbeAuwkqfw4eR/gGdvzSM7WHra3IhXz/3ozR24ESbL1w8fLkq6QtAD4d7KaFrA+8M+yjG3+8/l8vSuYCPygmXNWZpCk6ZJKkr5TUAPbAPifQr8FzezbRNITkh6TNJbWsRkwUNLvJM2Q9LOs/rUMOQViDdJnhaThpKjmRRXm/CbwVUn/JH0+F9ieX5jrOEnPkFIbvl6OUBfa1yXJ9F7T7HrVcTXaNgO2k/SnfE+PqbC3Suu1OC4IgiAIuotwcLuH6/OfjwKfkrRyK8a8ZfuOfPwE8I9y7ivJadqo2kDb75Aeg4/Ol8YBk/PxqsCVkuYAfwQ+RZJ9LXOT7bebzTfB9gbAGcAFrbC9U5F0KLC4cD+KvAAMsb01yZH/CstKyFbjCWD97OgfBpwlaY9WjOtPioQfRIqmb0DK8S3auxkpSny47XclrUjKsT2u/IWgGceSpHzXAz4LfEPShykTti+1vSmwHfC9HM0uchRwd/PIea1xNdr6k74EfJ6UvvBdSTu3Yr3WjAuCIAiCbiEc3O5hEXwY5YSUGvI+y97/5k5vY+F4SXmOwnlL6SWTgbHZcfkCcFO+/mNgIbCl7eHAn5ut/Va1CW1fA+ya5/w7sF7OKy3nl34qX69IjpTOlPSHFmxvzi7AbpLmS5qfrz0paTPbjbbLj/NfBK4lOaCQIrafLsyzQdk+22+Uo8E5sn1rYVwtFgC3237d9mLgNxRkbSVtTHp57VjbD+fLnyQ5rndm+78JHCNpUm7/BullPWy/APwO+IhzaHsOKUq+S7Omo1k2XaC14yq1LQCus/1Bvp/38VHZ3krrtWZcEARBEHQL4eDWj4XAipLKkdjRtTq3lexcDQLOI+WhvpOb1gD+bvt9SQ3ATtXmkLS6pPUL5/sD/wv8b3ZiZgKH5+bDgRm18m9tb5tfUqq6ZpVxJ9geYnuo7aH58ua2n5K0To6QImlV4IBsF8DNwJckrS3pY8AxJIcUSZ8spzJI+gTwxcK4WkwlOdsr5fF7AbPyPJ8B7iFVarirYP8C22sV7L8YuNz213OXeaRcayQNJH0mc/P5ZuV58ktzWwJPFa7tQMrf/XC9lsa1MOfUgi2rZVtmtbReS+OCIAiCoDuJl8zqRHYwTwbuk/QSUOnxe0e5GvgRyzqx5wLXSPoaqUTWQzXGrwbcmB2WJSTndn/b5UoGxwFXSzoLeJX06LrV5JfpHialTaws6R+kPNsrJR0AHGB7QgvTfB44R9ISYEXSS1yXANj+m6QfkVJDAO4FymW7vgIcL+k90t+Dq21Pa8ku23+SdBfJGV5CSnUoq4b9hJQ7fY6kc/K102zf08IexgG/kHRK3sP1BQf5bEmbk0tvkZznpwtjjya99NY89aHWuFptFwGTJD2Zz6fYvq8V67U0LgiCIAi6jX5NTZWqLgVBEHQPpVJpKDDvv164IIQeKtAdQg+lUqlL1di6g96+h95uP/T+PYT99ae8h8bGRubOnQuw4ciRI+e3Z66I4AZB0COYuOfVDBjwkYIUfZ6Q6g2CIGg74eD2YiRNoHK5sHGFigvdSq6Pe2+Fpltsn1PhehD0eroychLObRAEQdsJB7cXY/sK4Ip621Ekv3wWEq1Bn6KrnNvFS95jpf4rdsncQRAEyzPh4AZB0CMYd99pvLbkzXqb0aO468Ar621CEARBryQc3KBXIWkmsH2WyO1xSBoH/Mn2X7ppvQtJFSGGAsNszy20rUyqbrAHqY7yI4XSZG1dZzJJSvqSrNK2ku17C+3XAruSav4OtF21nnIQBEEQdDVRBzfocspiEJ1BrqPbI53bzDhgk65eRFL5y+mtJFGI/6nQ7ackx3YT28OA73fS8ruQ6gYXuZJITQmCIAh6CBHBrQM52iVgAPAsMB64EfhFoRbrfsAptnfNhfmvItWlnUmS6T3X9u1V5t+FJCbwGLA90AQcVq51Kuk04Mjc/XHgpGoRN0n7srTOK8BmwCG2p0kaC5xA+j16HTjetnMUcwzwJrAxMEbSuiTRif7ASySlr2cliaS6tmpum2z7whr3rokcIcyqYL8GdgfWA04H1iGJZnwCGG/7oVzXdjqpLvCeQD/gBNtVFdUkDQJ+BmxBUnr7PfBt20skPZDv2/Yk9bbf2D5d0tHA1sDPJZ0LnGr7/gpzjwG+YvugfL4CSQlsR2B14Jekz3plYJLti3O/ySQFPAEDgRFltbR0G5dZY3VSXeIh5brFtv9Vbb+F+afbvqTSeb42jFT/+GNZ2vh62+fb/l0lO4IgCIKgHkQEtz6cbHvrHFV7EjiNLK1b6HM0yakFuIbk/DaQHNdRrVhjc+BS21uQ1LvOBJC0D8m53QEYRnIqq0b2bN+Zo6YjgIkkR/EeSTsBhwI72x4JXMCy8q3bkRy8BpIU7DXAEdmeqSRJXUgO8m22h+e+bU06HGB7e9Jj+suB92xvA5zBso75YGBWXv8k4DpJtWpS/Qx4MM81guQ4jy+0b0CKnG4JTJC0se2rSPfnG/mefcS5zdwC7CRprXy+D/BMlgyeD+xheyuS1O3XJX2uMHYEsHf+PGrxWeAV4AeSpkt6QNLnWxjTIlna91KSkMMI2+d3dM4gCIIg6GzCwa0PR0kqSZpDijaOYKnTM1jSYOALwM05kthAcgqxPR2Y3Yo1bHtGPn6U5PBAyse83vYbObI3KV+riaS9gG+T1MUWAfsDw4HHcl7s+cD6hSEP234uH29Lci7LcrBXASOyLO1DJAfxR5J2A9pa6f+G/OcTpChw+bxEinSXWUxWMbP9APAuKRJajQOA7+S9PQGMZNnUgxttf2D7deBplt7fFsmyybeyVJ55HOkLDnkPV+bfjT+SIsTDC8Nvsv12K5bpD3yGJJ+8NelL1C359ylp70cVAAAgAElEQVQIgiAIlmvCwe1mcuTzeFIUbhgpsrpydnqmkZye0cC0Zo5MWyXnFhWOl9CBdBRJw0lRuwNtv5wv9wN+VY7u5gjsBoVhrXrJyPbNJCnh50gpBte00bxFeZ4lxXM6uGfS/r5U2N8mtr/TfN0OrDUZGFv4MnNTvv5jYCGwpe3hwJ9JqQplWvvy1gJSOsN1ALYfA16mdn7w+yz7b8LK1ToGQRAEQU8mHNzuZw1Svuor+RF58bH3ZFI0bxw5PcH2G6Q0hsMBJG1FSi1oL/cDX5U0UFI/YAJwX7XOktYDbgbGNKsM8FtSJHpI7tdfUrVioI8CwyVtms/HkiKLb0raCFhoezLwQ9Jj+a5gJXLENH/JWAV4pkb/24DTyy/ISVpL0oatWOcN4OMtdcq5s4NIecm35i84kH4//m77fUkNJOe/zeQvIr8n5RwjaRNSmsWzNYY9S05/kfRJUlWESrRqj0EQBEFQL8LB7X7uJkUr/wI8SHr8DSzj9AwqvzyUOQr4Zn5sfSowh+Qktxnbd5Ee1T+S5wE4t8aQCcDawERJM/PPrrYfAr4H3CZpFjAXOLDKmi+R8n6nSppNegFtTG4+FJgjaQbwC+Dk9uyrFbxCSouYTXqJ63Dbi2v0/yYpMjsr3/e7SS+ytcQk4Kx8n1pK/bgaOIal6QmQPotjsp1nk1I4qiLp55L+AQwB7pf0ZKH5OOCMbP/1wJG2a6WAXA4MkfQU8F+klxQr8f+AUXmPp2c7bsl2AFjSPbXsDoIgCIKupF9TU1uffAfdTX4j/m3bTbmiwgOAbL9aX8t6B+UqCrbXaqlv0P2USqWhwLwLX7gmhB6a0V1CD10pNdxd9PY99Hb7offvIeyvP+U9NDY2MnfuXIANR44cOb89c0WZsN7BDsAFOaUA4JhwboPljcl7/oQBA2oVtuh7hFRvEARB+wgHtxeQFaPubX5d0m2kclVFFtg+oK1rSDoL+HKFpi/afrGt83WEzrbF9nzgI9FbSSNYNj2gzCW2r2jrOhXmnwD8W4WmcbZndnT+9iBpHSr8LgG32D6nu+1ZXuiqyEk4t0EQBO0jHNxeTHsc2RpznQP0CAenu2zJTmaXqW9lJ7nDjnJnkr8ghOJYJ9NVjwUjghsEQdA+wsENgqBHcPS95/Paknda7tiHuPNLP6m3CUEQBL2SqKIQBEEQBEEQLFdEBDdoNZKuJdVG/SQw0PZbhbYmUtmxD/KlI7Osa7W5ViXV+h1JEhg41fbtFfodCJwFDGCpuMR/5Lbzgb0L3TcFvmv757nyxC9JUrorAlfYvrAw7/dJ9YYBJtv+UcGuy0nqYf1I5c8m5Jq9tdrKUsZbAnfaPrjZXquN2xv4CUnIY0WSwtmZWWWOrO72U1LdXoDRtmd1YH/jSHLP83PbPNsH5bZ1gcuADfOc/27717ntbJKs8vN53B9tn1hY7yTgROA9YEkrpISDIAiCoMuICG7QIpLKX4SupHb+5g4F5a+qzm3mVOAN2xuRZH+vyE5bcxYC+9tuIFWTOD4LNWD79PJ6JEGDD4Df5HFnkOR5tyA50UdK2i7vZ2fgEJIEcgNwSL4G8HWSKMSw3NafpDzXUtuLJCnjb1XYQ61xDwNb5T2U97F/tnM94FfAEbY3B7YG5nVwfwD3Fz6ngwrXf0Yqp7YFsDPwY0lF+eUphXFF5/bLeb1RWZ1vrwr3IAiCIAi6jYjg9gEknQkMtv2tfD4YMElR7EySJOsKpIjd9bnPA8BMYDvgf4F9bf8ut3WGWV/N62P7r5KmA/sANxY7ZYnZ8vHrkp4GPg38odl8R5Ict4X5fDgpctkEvC3pQeAIkqraV0nO2rt5P1PytYdIkdRVSRFMgNWAsoBB1TbbzwPPS/pchb3WGleU3l2Z5AiXo+AnZDud+74LvNvB/dViOHBRXuslSTNJQhz/0cK4U4Dv234zj/1XC/2DIAiCoEuJCG7fYApwWCESO5okRfsn4PO2twT2AC6UtGZh3Gdy+76tXOeBrG51XpYhrsUGwP8UzhcA61fpC0CW+t0O+F2F5qNJ0c4yJeBgSStKWosUVfx0K9a+DHgT+Ff+ed321Fa01aLmOElbZ+WyF/Pe7shNmwEDJf1O0gxJPyvc1/buD+AL+XN6SNL/KVwvkX5P+mVZ4h0Kc5LbZku6V9L2heubAdtJ+pOk6ZKOacU9CYIgCIIuIxzcPoDtBcCTQNlRHUeq/7o2cJOkucA9wCeAYnh2qu33W7nMBra3Jj3a3gz4fsctX4qkTwLTgBNytLTYtg2wDlDM4T0feBmYTpKpfYCU69sSZXndT+aflSSd2oq29s6J7XJawPqkdIOdclN/YEfgIGBbkuN6Wgf3dzvpsxpBkkW+shB1PgX4/0iR+58D/12Y81Jgw2znBcC0/CSgbOf6wOdJv2PfbZYSEQRBEATdSji4fYfJwFhJw4CPkx7x/xfJMRqWHZ5/kB6Tl3mLVmL77/nPN0i1X3dsYcgClo0ObgD8vVLHLE5wP/BT2zdW6DIeuKbojNt+x/aJtofb3gNYAjzVirWPI4keLLK9CLiB9GJdS221aNU42y8Dd5HyWct23m77dduLSfnF23Rkf7ZfLqcu2J4B/LEw50u2x+Q59wcGlue0vdD2e/n4vjxfQ2G962x/kOvs3leeMwiCIAjqQTi4fYdbSNHVU1iau7kGMN92k6Q9gY3aM7GkNSWtko9XAA4mRQFrcSNwbB6zMTAKuLvC3INJDtMltq+s0L4KcBjLpicgaVDBpi1IUdBfFtY+StIquc9RLH05bR6wV35M/zFSlYa5rWirRdVxkjbJ15C0GikPufyC3lRgN0krZZnmvYBZHdlffnGtfI8+TUr5mJ3PB5fTWHL1hmHZhubjRgBDSXncZTv3Luxhp7KdQRAEQVAP4iWzPoLtdyRNI+Wqbpgvnw78UtIPgcfJjk41JN3C0sicJc21vRepPNdluVTYiqTc3pZSFC4AJkt6lhR9/Hr5JSVJ5wDP274027gJcKykY/PY/7R9VT7+MvCM7aeWnZ7PAL+R9D6wiFSJ4Pl8Lx7Ie3ky951i+8F8/ENgEksd11nAv7fUJmkoqSLCqsDKkv4B/CA75bXmPBAYl+3sD/w/svqZ7T9Juov0ZWEJ8ATw4w7u70Sl0mvlaPcZOZIL6bP9uaQlpPSH/W2XlRd+LGlktmMxqQxc+YW+i4BJkorr3UcQBEEQ1Il+TU1N9bYhCII+TKlUGgrMa2hoYMCAlt5N7Ft0l1RvqVTqMrnh7qK376G32w+9fw9hf/0p76GxsZG5c+cCbDhy5Mj57ZkrUhSCIAg6SKlU6pJ5u8O5DYIgWB6JFIWgy5C0L0sfqRc5w/ad3W1PEHQVnRE1WbzkfVbqH/8kB0EQdAbxr2nQZWQnNhzZoFUcfc8lvLbk3ZY7LqfcedCZ9TYhCIJguSFSFIIgCIIgCILliojgBj2CLAu7fblGaxeu8yVShYY/d+U63YWk75FkeJcA/YDzbN+Q2+4H1spdVwA2B4bbrlkto8o644D9bB+cK0Z80fakQvupwDHAxsABtm+vOFEQBEEQdAMRwQ3ajaT+nTWX7RFd7dxmvkQNEYLO3FNXk229xPYWWW55X+Dystyy7T3yfR0BnAk82R7ntgJDga83u/ZgXv+hTpg/CIIgCDpERHA7EUnXkqRuBwDPkhS2bgR+YXta7rMfcIrtXSVtBlwFrEaqdboRcG616JekXYCLgceA7YEm4DDbT+f204Ajc/fHgZNsV1Qjq/AC2GbAIbanSRoLnED6/XgdON62cxRvDPAmKVI3RtK6wHmkGq4vAcfaflaSSOppq+a2ybYvrHHvmoCBtt+SNB/4NbA7sB6pFu46wGiSnPB42w/lSOJ04GpgT1IE8wTbf6iyxl7AAcAekiYAPyOpcP0cKAFbAmdKGkSSsV0pDz3V9n/nOeYDU/J6nwQutH1JFmu4BNgNaATesl1RzU3SBsCfgfXL6mCSbgJ+C1wL3AEMBlbJ/Y61vbjS/bddFNRYnfQ7UemL63iaiWFUsGscOUpb6bzARGDDHHV/1vbBth/PY2otEQRBEATdQkRwO5eTbW9texipyP5pZIncQp+jSU4twDUk57eB5LiOasUamwOX2t6CpE51JoCkfUjO7Q4kBar+1BBbsH1nIbo3keQo3iNpJ+BQYGfbI0mCDEXHaDuSw9cAPJ/3cES2ZyrJQYPkIN+WZV8bgI+okLXAANvbA18BLgfes70NcAbLOuaDgVl5/ZOA6yRVLKZq+x7gNuD8vPcpuWlzYFK+djtwD7BdjooeRnKgi6yabdsFOF/S6sBwkvzuZraHA/tV25jtBSTRh33gQ7W2XYCbSKkGo21vTZLC7U9yTst8eP/Lzq2k4yQ9A8wgCWa8UlwvfwnZg/RZdQYnAk/l+9Xc+Q2CIAiCuhMObudylKSSpDmkaOMIkkTuTlkGdTDwBeDmHCVsIEuh2p5OC0piGReUpx4FPpuP9wCut/1GluGdlK/VJEc1v03Km1wE7E9y1h7LEbrzgfULQx62/Vw+3pbkXJZVxK4CRkgaSHpUPUHSj7Ls62ut2FuRG/KfT5CiwOXzEstKCi8mRXux/QDwLimK3hb+avuRwvlnSc7+k3nddbOTWOb6vN584FVgCPA3korblZKOpGUmA+Py8WjSl4G3SX8nT833fjYpIjyiMK54/8l2XGp7U5Lz+738e1bkKOBu2y+1wq4gCIIg6PWEg9tJ5Mjn8cDeOYJ7JrByljqdRnJiRgPTsiNTpq1ScosKx0voQJqJpOHApcCBtl/Ol/sBvypHd3MEdoPCsIopD82xfTOwE/AcKcWgrdHDRXmeJcVzOrjnKjTf03XAL21vDmxFkrVdubltRXtsv06KBF8PbAE82cwpbs6HX3xIjm45qj8a+DywU/49+mWztavef9tzSFH1XZo1HU0L6QmZ91n234SVq3UMgiAIgp5MOLidxxqkfNVX8iPy4mPlySQnZhzZkbH9BimN4XAASVuRUgvay/3AVyUNlNQPmADcV62zpPWAm0l5nH8pNP2WFIkekvv1l1Stiv2jwHBJm+bzscAM229K2ghYaHsy8ENqvNjVQVYiOYXlLxmrAM/U6P8G8PEW5lwDmJePx5NyqmsiaW1S6sI9JIf+deAz1foXvvicBwwq5A2vAbyc7+HHyXurse5mheMNSXnETxWu7UDa710t7YGUN76FpAGSVgKqpR+05h4GQRAEQd2Il8w6j7tJLwD9BXiZ9Ih+GwDbD+eUBGw/XBhzFPArSf8XmJN/Xm/P4rbvkrQFUH7UPh04t8aQCcDawMTCi0Hfsv37XHrqtvyW/kqkF+U+okVq+6X8OH6qpBVIL5mNyc2HAkdIWkyKUp/cnn21gldIaRHfJUWfD7e9uEb/a4DJkg5h6UtmzfkmcKukV0mf6ysV+jRnfVIFgxVIf6/uIn0BqMVk4A8smys9BTgw59S+mNtXqTHH2ZI2B94jRZO/UX7pMHM0MKUQCa+K7UdzabEnSZHgWaQX6ZozG7CkucAzuXTYd0if8dqk+7uIlI/8RkvrBkEQBEFn06+pqa1PyIPOIr+c9LbtphyJewCQ7Vfra1nvoFxFwfZaLfUNei6lUmkoMK+hoYEBA1oMli+31FOqt1QqdYrccD3p7Xvo7fZD799D2F9/yntobGxk7ty5ABuOHDlyfnvmihSF+rIDMFPSbFLu5jHh3AZB76NU+sgDjjZTL+c2CIJgeST+Ra0jtu8F7m1+XdJtwAbNLi+wfUBb15B0FvDlCk1ftP1iW+frCJ1tS65i8JHoraQRpMf/zbnE9hVtXae99BQ7mtOZv19BoqNRk3pGb4MgCJZH4l/UHkhnOhq2zwHO6az5OkJ32ZLrw45osWMfsaM5PdWRHX/3Fby2ZFHLHZdD7vjyKfU2IQiCYLkiUhSCIAiCIAiC5YpwcIMg+BBJMyXVqtoQBEEQBD2ecHCDHkkuURa0gs68V1nc493Omi8IgiAI6kHk4PYSJF1LkqAdQCrIP55Un/YXtqflPvsBp9jeNZcduwpYDZhJkrc91/btVebfBbgYeAzYnlS79rByTVVJpwFlCdrHgZNsV1TVkrQv8OPCpc2AQ2xPkzQWOIH0u/c6cLxtSxpHqqH7JrAxMCYrgZ0H9CfV2D3W9rNKhXsnkyR8+wOTbV9Y4941Ad8DDgIGA9/JSmtI2rvSGjXmeiDvf3vgU8BvbJ+e2zYCLiPVgn0fOMP23bVs6KH3aqDttyTNJ8kg7w6sRxKwWIckPvEJYLzth8rl2oCrgT1J9YhPKIhXBEEQBEG3EhHc3sPJtrfO8q1PAqeRHJexhT5Hs1Ty9RqS89tAclxHtWKNzYFLbW8B/IYkN4ykfUjO7Q4ktbX+LCtOsAy27yxL/QITSc7PPVlp7FBgZ9sjgQtYVkJ2O+DUbPPzeQ9HZHumAtfmficAt2UZ4Qbgylbs7Q3bo/I+fp73tU6NNWqxAbAzSTVsgqSN8/Vrgal5rjHAr7PCWVUbeui9KjLA9vbAV4DLgfdsbwOcwbKO+WBgVl7/JOC6rOgXBEEQBN1OuxxcSbtK+kJnGxPU5ChJJUlzSBG0EcAtwE6SBksaDHwBuDmrpjWQHB1sTyepT7WEbc/Ix48Cn83HewDX237DdhMwKV+riaS9gG8DB9heBOwPDAcekzQTOJ+kAFbmYdvP5eNtSQ5TWXb2KpJi2UCSStwEST+StBvwWiv2dn1hX5+StHILa9TiRtsf2H4deBr4bB4zgqVSzE+RIufbtWAD0OPuVZEb8p9PkKLA5fMS6alAmcWkaC+2HwDeJT1xCIIgCIJup1UOrqQHJe2Yj08j/Uc9VdIZXWlckMjRvOOBvXME90xgZdvvANNIDu9oYJrttwtD2ypTV6zRtIQOpLBIGg5cChxo++V8uR/wq3LEMkcVi/VYK6Y8NCenF+wEPEd6bH5NK4YtymPLkrUdSc9p732qaEMPvFe1bC7vvUO/H0EQBEHQlbQ2gttAijoBHAPsSopMHdcVRgUfYQ1SDuYr+bHv+ELbZGBc/ilHD98gpTEcDiBpK1JqQXu5H/iqpIGS+gETgPuqdZa0HnAzMMb2XwpNvyVFoofkfv0lVauQ/ygwXNKm+XwsMMP2mznXdaHtycAPgW3aua+qa7R1ojxmZp4DSZ8jRWAfrTWuF92rlliJ9CWr/IVsFeCZLlorCIIgCGrS2gjMx4AmSZ8F+pUfhUpas8ssC4rcTcrp/AvwMumx8zYAth/OKQnYfrgw5ijgV5L+LzAn/7zensVt3yVpC+CRfGk6cG6NIRNIL1pNTO84AfAt27+X9D3gtvzm/0qkF+U+onNq+yVJR5KeFKxAenFqTG4+FDhC0mJSlPrkdu6r1hrt4QjgMknfIr1kdqTtl1oY0yvuVSt4hZQW8V1S9Plw24u7aK0gCIIgqEm/pqaWn2JL+i3wd+CTwHO2T83O7v22N+xiG4N2IGl14G3bTbmiwgOAbL9aX8uC5Y1yFQXbH5FNbg2lUmkoMK+hoYEBA/rme2n1luotlUodlhuuN719D73dfuj9ewj76095D42NjcydOxdgw5EjR85vz1ytTVEYR3o5ZTZwdr62KfCf7Vk06BZ2AGZKmk3KmT4mnNsg6BpKpY8E1ttEPZ3bIAiC5ZFW/atq+xVSWaDitTu6xKKgU7B9L3Bv8+uSbiOVuSqywPYBbV1D0lnAlys0fdH2i22dryN0pi0VatOWOcP2ne2xryfR2Z+b7flAu6K3ywsdiZrUO3obBEGwPNKqf1Xzi01nkV5aGmz745K+CGxi+5KuNDDoXNrjyNaY6xzgnM6aryN0pi3Zie31jmw1etLnVmT83VN4bUljvc3odu748r/V24QgCILljtamKFxEqqRwBEtLTz1JKl0VBEEQBEEQBD2G1jq4BwGjbT8CfABg+58k+c4g6FQkzZS0Sr3taCuSdpG0RNK/Fa5NlDQ77+lxSbt3ky1X5HJdSPqSpA6VB5M0X1JDPh4naZNC23qSfi/pdUnTO2Z5EARBEHSc1jq4i2mWzpAlSF/pdIuCXkkuZdUpZGGDdztrvq6kvO+sGvYT4K5mXc6wvUWW4p0A3JBrCXcptifY/kM+/RKdW/92HLBJ4fwtUgrT6E5cIwiCIAjaTWvfbLgRuDrX90TSJ4GLWSo9GjRD0rUkqdIBwLMkcYYbgV/Ynpb77AecYnvXXMrrKmA1kmDARsC5tm+vMv8upM/gMWB7UurIYbafzu2nAUfm7o8DJ9muqH5V4aWqzYBDbE+TNBY4gfS78jpwvG1LGkeqtfomsDEwRtK6wHlAf1It1mNtP6tU4HUySeq1PzDZ9oU17l0TMND2W5LmkyRgdyc9MTgdWIfkTH0CGG/7oXKpKuBqYE9SLdYTCk5e8zU2AP4MrG/7vXztJpLAwrXAHcBgkmDBn/NeFlfaN+nz+hlwAbBfcZ0s51vm47RCXS5/dl8h3fN/kipgLJR0Nql6ySCSg1kiSfj+B/Bp4Bbb38lzPABcCLwHHAD/P3tnHmZHVfT/TwwQtgQwiCiIQeEtlgkJDGFHFpFNFtGALIEECKsCLiiIioIoKIi+bD9eZEnCLosksm8CogTJkISE5ctiYkRA9p0EEvL7o6qZnuHeO3cmmcxMUp/nmSfdfbpP1+l7J1Ndp0592c7MRgJnShpT5b7TgV0kTa20H8cOBDYEzjKzU4BjJd0J/DW+k0mSJEnS5dQbwT0BmIaLBSwPPAU8RzdcqNKNOEbShiGt+yhwHO7kDS+dcyChPoZLqJ4tqQF3XIfUcY91gfMlrQf8EZfwxcx2wp3bzXAFs97AT6t1IunmQhIWOBd3FG+LKe69gC9JasQduItLl26COzgN+PfhUmC/sOcK3FEEd5DHhdxsA3BRHWMr00fSprjT9wfgA0kb4d/LsmPeH5gc9z8KuDIWSFYa8wxgKrATgJn1B7YGrsVlaPeVtCGee96blupxH41b0qR43stJurbSvczsZDN7BrgBf3Go6uSa2TDgi8AmkjbAF7v9tnRKI77Y03Bn97QYw3rAcDNbs9U4bwPGAafFZ1zRua0XSZfg34+jo78756W/JEmSJOkM6i0T9j7wXeC7kZrwcq0/0gngMqv74QpUy+AqZCcDvwtnCmCrOK8f7khdASBpQtSvbQtJmhjb44FdY3s74KqQ7MXMLqCOmsVmtgPwPWBLSTPNbFdcbvbBUNnqBZTV6+6X9Exsb4w7l4/F/iXAeTF1fx/wGzNbGvhL/LSHq+Pfh/EocLHfhEe6C97Ho71IusfM3sMdwWrPchQ+3T4OjwiPk/ROpB0cG45r7xjzu5XGbWbL407mV6oZL+lE4EQz2xZ/DlvUUPnaDY+QPhzPvIicF9xWRIXjOzJZ0ixglpkJd46fqmZLkiRJkiwK1F18MZyTNYBlgTULWVFJf+8c03ouEfk8AtgsZFT3BQ6V9K6ZjaU5V3FsOFT9Yr+9Lw0zS9tzaMfnWcHmQcD5wA6SXo7DvYCLw0GrRMWUh9ZIus7MHgC2x1MMDqJ9krgzo5858b0rxj1PYwaup/mFYwTwnTi+L7AF7ui/ZWYn8PGc04IGXOHvH2HbisCuZvbJKMf1EZLujs96IBUkd4NeeGrKxVXaW3/m8+07gMsLl2d1lpyHvpIkSZKky6grRcHMDgBeAO7Go2fFT+bgVmZ5POr2SkyRl6e3R+HO1AgiPSEirY/iU8+Y2Qa4E9RR7gS+aWZ9Y0HTSOCOaieb2SrAdcAwSU+Wmv6MR5hXjfN6m1m1ivbjgUFmtlbsDwcmhoO4BvCCpFHASczfBU9lliBeHuIlYyngiWonS3oXGIvnDfcr5esuj89SvGVmy1Fj8ZSk+yWtJGmApAF4isPPJJ1sZr1KzwMz2xDPH/5njTGMA440sxXimj7x8jEvvInn/7bF00RqTFR7+PQ89pckSZIkXUK90Z7fAN+QVNVJSlpwKx6hfBJ4GZ+i3wjcISoitpLuL11zAHCxmf0Iz3WeQsup6bqRdIuZrQc8EIcmAKfUuGQk8Cng3CIyD3xX0l/M7MfAuJi2XwJfKPex6GNEqvcHrjCzxfBFZkWUdi9gPzN7H49SH9ORcdXBK8BgM/shHgndp0YqQMEo4K+0zFEeA+xuZk8AL0Z7R8qW9QIuMLNP4tHR94C9akkmS7rUzFYE7o3P4hPAecDkDty/4FJglJntSY1FZvgzGG1mR+EvszOqnHcB8Fsz+wFwLJ5y8i98QeVyZvYscKGkn8+DzUmSJEnSYXrNndv2rLiZzQC+WKw2T+Y/ZrYs8I6kuVFR4R7AajlDSTNFFQVJi7RkbE+kqalpADCtoaGBPn0qrglcqOkOUr1NTU3zJDfcHejpY+jp9kPPH0Pa3/UUY5g1axZTp04FWL2xsXF6R/qqt4rCT4EzI7KUdA6bAZNi4dBVeGmodG6TpAfQ1FQtpbptutq5TZIkWRip93/WogLAkaUp7F7AXEnzrcD/ooyk24HbWx83s3HAaq0Oz5C0W3vvYWYnAl+v0LS9pBfb29+8ML9tkTQdX+DV+j6D8TSE1pwj6cL23md+EYpfrX//xks6vJPv27rmccEJkm7uzHsvzLQnatIdIrZJkiQLO/X+L3spnpd4NZ5HmCwgOuLI1ujrZLpJ7eIFZYukScDgzr5Pe4kau11x35vx2rrdjoNuvZLXZ7eVMt3zuekbh3a1CUmSJAs99Tq4/YETs/ZtkiRJkiRJ0t2p18G9BFfGmicVpCRJ2k9I9C4r6dgKbSNwOd2hC9qukg0D8BJjU0uHvyzpla6xKEmSJFnUqdfB3Qj4dpSM+m+5QdKX5rtVSdLNMLPekuZ0tR3djSgJB/B6SD0nSZIkSZdTr4P7h/hJEgDM7HJcBrcPHr07CK+Re7aksXHOLsD3JW0Tpc8uwWWLJ+GqeKdIurFK/1sDvwceBDbF6+fuLenxaD8On1UAeAg4Sk09/zkAACAASURBVFJFZbUKC6vWAfaUNNbMhgNH0iyJe4QkRWR0GPAWsCYwzMxWxkUheuN1fg+T9LT5ystRuIxwb2CUpDOq2LI0MBpYF/gAl1veK/q+EuiHK4jdJOmHFa5fAjgb2BavsTyx1NYb+DWwYxy6FTiummNuZhcCUyT9b+w34EITX8RFR47Bax8DHCvprjhvOl7pY1u8XvMvKvWfJEmSJF1FXQ6upNGdbUjS4zimkPQ1s1OA43AnbziuDgZwIKHWhi9U/J2ky0LR68E67rEucKCkw2L24Ce4YMROuHO7Ge6AjsZL2R1XqZPywiozOyTsui3UzvYCviRpVvR7MbB5XLoJMEjSM2a2Eq4Gt5Wkx8zsYOByYGPcQR4n6dS4xwo1xrQDrpq2TqtzXwd2lfS2mS0e9u0o6dZW1x8GrI476YvjIiLTo+1QfEHdBrF/Sxz7f1VsGQWcBfxv7B+IO+dzzew24MrYNuAuYNXStf0kbRRjGAD0i8oQvXDn94zM2U+SJEm6irpr1ZjZp/FUhRXxP2IASLq4E+xKuj8HmNl+eIRvGZpLyf3OzPrHOVvFef2ABuAKAEkTot5vW0hSEaEcD+wa29sBV4XEMWZ2Ac1OWlXMbAfge8CWkmaa2a7AIODBKH/XCyg7p/dLeia2NwYmS3os9i8BzjOzvriT+ZuIzv4lfqoxGVjbzM7FxTxuiuO9gdPNbLOwY2XcWW3t4G4DjA7RlQ/M7DJgi9JzGVWot5nZJcAeVHFwQ1Wvr5kNBB7Ho7abRvMXgStDxvkDYGUzW1nSC9Fezsd/HlhV0ovxIjAOeA3osjJsSZIkyaJNXUIPZvY14Bncgfk/4Kj4d/9a1yULJxH5PALYUdJAPLK6pKR38ejtvvEzVtI7pUvbG9GbWdqeQzteyCrYPAg4H9i9iDzjjuTFkgbHzyBJ5ZrDFVMeWiPpOmBL/HfkeDxaXe3cf+KR6Ttwh3SymS2JO94rABtLWg+4AU9V6GxGAyOAnYDHJf0rjl8JnCdpXTwiPLuVPR89G0mzitrF8e/lNEfBkyRJkmSBU6+S2Sn4VPH6uJzs+vjUZ8fle5KezPJ4vuorZtYHz78tGIU7TCOI9ISItD6KRwgxsw2AgfNw/zuBb0b0sRcwEncYKxJRyOuAYZKeLDX9GY8wrxrn9TazahX7xwODzGyt2B8OTJT0lpmtAbwgaRRwEj7TUc2WVYE5km4Avgt8Cvgk/kyfj8jyKsDuVbq4G9jfzBYzs6XwF4mCO4HhZrZ4pDkMp8ZzCcbgn8tImtNJCHumxfZBeK51tTGtFPcrcox3w/OskyRJkqRLqNfBXU3SNa2OjQYOmM/2JD2DW/Fo5ZPAvcDDRYOk+/GFUv1iu+AA4DtmNgU4Fl+c9EZHbi7pFuAy4IHoB/wlrBojcUfyXDObFD/bSLoP+DEwzswm42WuKjqWkl7CZyyuiPSKYfEDnsc7xcwm4gvAjqlhy0DggbjfP4BTJT2H58JubmZTgYvwnNdKXADMwFMK7sYX2JXbHsEXnk2M7ZqLQyXNAB4DtgauLzV9B7jBzB4GvgDUKvm1BTAxxjQh7n1OrfsmSZIkSWfSa+7ctmeNzexpYHNJ/40/4kfiK7jHS+pf++okATNbFo/+z42KCvcAJum1rrUs6WqampoGANMaGhro06dqoHihoTtK9TY1NbVLbrg70tPH0NPth54/hrS/6ynGMGvWLKZOnQqwemNj4/SO9FVvBPcPNC9k+R2+iGYycF5HbposkmwGTIro51XAIencJgsLTU31Z2t1N+c2SZJkYaTeMmG/Lm2PMbN7gGWKmqRJ0haSbgdub33czMYBq7U6PEPSbu29h5mdCHy9QtP2xSKoBUU3s+V8vORZmdmSNlyQdizM1Bs16Y7R2yRJkoWRdv9Pa2afAJ4ttiV9ON+tShYZOuLI1ujrZLzSR5fTzWw5vKttqIeDb72W12d/0NVmdCo3fmNEV5uQJEmySFCXgxur3s8F1qO5VFAvvOxT784xLUmSJEmSJEnaT70R3NF4SaWDgHc7z5xkXgmJ2d/TrG41TdIeHexra1yRakMzWx44VNJvSu0/Br6J16jthVcEuLod/ffB6+ZuCCBpxRrn3oOnMrwZh/5X0iUhKnEpLkzwPvAULqH7UqvrL8aVuvqGWthn8O/0YvhL2hMxvtfaaNsMOAMvowUu1PDDsmpX1LVtAt4r0gBCQe0oml8MfyPpslY2Gl6B4DxJx5aOHwV8CxdcmCNpcBw/F/gyMAuvS3uMpAmt+hyOl27btZBFDqndmTTXGT5O0m1m9j94fevP4HVvHwKOlPReXLcrcHo8lya8dOC7ZjYUr4VcsCpwn6RKKRpJkiRJ0unU6+B+HvhxSm92b8ys+DzvlDR0Pne/PPBD4DelY+dI+mXc+7PAE2Z2ezsWj83BncWX8RqubXF04aSVKJzFe8KO04HTgIOLE8Ixa/3dfRmX6H03zvkdLvf7vTba3gSGS3oqHPS78HJhZXGHXxJ1c0vHngK2lvRq1MKdZGb3S5oe9+iNO5c3lI00s68DewJDoubup0vNtwDfkfSBme0CXI07+sW1q+LSvuNbP0hgqKSprY69D3xP0sRIRboSL+n2i6iC8QdcBe4pM7sw2k6WdC1wbem+EwnVuiRJkiTpCup1cP8EbA/cNi83M7O5eN3RPYD+wA8kXRda9hOKCF55v9jG/7juCCwF7AccjsunvoerU71AFSJidRke7VoFV5taCS+S/0ngoKiJipntHDYuif/B/66k8RFdGwUsjUf1Rkk6w8yWwB2arfBi+I8AR0SU8FC8mP8svGLFXpKeqGLjU7jTMTn2vw00SjrQzM6I/pfAna+DJP2r9GxGAdvidVBnVui+KuUobaX9EucCy5vZJOBdSZtJKtexXRZ3IuutzIGk2cCdMY4OIelVvORYwXhcZQ2AiPD+DP/sDypd9wEeES2cy2WJurxttE0t9TErnLnPl+63JbAmcCYlB7dwwGP7WTN7Ho90To/DxwM3xr2WLY3n+8BPJb0V1/631E/Z2X8AWLVVXvwF+Pfv19RBONvTY/tDM/sHsHY074T/Tj4V++fjMzst8owjnWlVXK43SZIkSbqEep2RJYE/mdntZjam/NOBe74paQheNP+sOq/pD9wfCmpFEfxzQ9K0Cfh2HX30kbQp8A3cWf5A0kbACcCvAMzsi3ikbidJjbhAwB/j+iOBcSHn2hB2gEc135C0kaRBwHPAj6LtdGDbmFIeghfor8ZoXHmq4ECalaVOkzQk+r+Slg5Lf+AhSRtIOj+ObRViBveZ2VfreDb18C3gdbmk7WbFQTM73MyewKfWD5VUSxBgXjndzKaY2WWh9tWCiDoeQUvn6lzgZ62c8fI1k4CXcKe0tbNWtS3aV8K/TzfF/jJ4esgRrc9tdd3WeES8KfYHATvgJfhasw6wiZn93cwmRKpDJb4N3FQ4t2Z2BPCopAernH+5mT1iZudF+klrG5fCXwiKZ7ka8K/SKTOAz1Xo9yDgcknvV7lvkiRJknQ69Tq4j+FO1d9wBavyT3u5Kv4dD3w28hXb4m1JN8X2w8Czkgop0CZgjTr6KHJDH8ajsMV++fod8Cne+8K5uRxYLKaF7wNGmtkvzGxb4PW4ZjdgWKGQFfvFNPHdwOjIoVylmPKuwhhgH3MJ1oG4A/TXaNvJzMaHytWxwODSdTNpdsLBo4CrhVN9DHCRma1NJyHpfElr4WWofhwR085gf0lr42N/gubPr8zZeC7qOQBmthfwfum78zHiOX0aVwY7vN42M+uLO3+/lTQxDp+Ov3j9p9r9QuRiDLCPpPfMJW4vAA6XNKfCJb1xR3ILYGfgh2b2pVZ97o3PRhwR+6vjL2cnVjFjy3hZGoLnBLdQHYtUl6uAuyXVHYmNlI19gYvrvSZJkiRJOoN66+CeNB/vOTP6nOOz/iyGL2gpO9utnd5Zpe05tJyGn0N942h936KP8vW9gFslVZIgvs7MHsBTNY7HI1XD4pojJd1d4Zqv407EtsBfzOxwuczsx5A0w8wexaeCt8ZTIOaa2efxyN4QSdNikVM5v/Gdcm60pJdL2xPN7G/ARriTVom2nn1dSJpiZs+F7ddVOicWRW0eu9+UpHb0/+/4d46Z/S/w8/J0fKRxrIkvpiqm6LcGto0UlYJHzWwnSY+V+v7AzEbjkf1yjnHFNjNbGn+RuF3Sb0unbwHsbF4Dd0lgBTN7JGYaMLM1gZvxRXCFjPFn8Beim+N7uTzQy8z6SToUj5ReGWN60czuwD/PIqVmDzxF5sul9IVN8VScx6PPlfEXnR9Jurj0LGeZ2XmUIt6RknE58BpwdGlsM4BtSvurAf+mJXsA/5T0CEmSJEnShXSXiuMvAIub2RqSnsajQF3B7cDPzGxdSY8CmNkQSQ+Z2Rr4H+9RkS9bpA+MA75nZg9ERK4vnoP4FPB5Sf8A/hHpD+vjC4OqMQqPvA2huTB/PzwX+IWYgq9Z09TMVikiiOEcbwKcUuOSfwJfMLMV8Kj0PlXOexNY2swWi9xZzGydwlGMqOH6eLS/IpK+Vcv2akREsX/JgdsHmFJybn8FNAJflfTRy5CkI/HUkqKfucC6kR/9OeC12P4EnmowJc6r1bYkXmFhvKQWEdLCkY3ztqZlbvMX8Bz2o8svOZJmACuWrvs5sKyaqyhcgeee3xcpEFviOfGYLyw7E/hK5M8WfV5B6SXIvALFGZJujD4Wk/SGmfUC9gYmxXmfwL+Dc4CD1XJR6a3AOWa2ZuThHk7LmQPwl76M3iZJkiRdTrdwcCXNNrNjgDvM7CUip7EL7HjKzIbh0a6l8EVdf8PLJe0F7Gdm7+OLqY6Jy04Dfg48ZGYfRttJuOM4KvIbP8SjXce3YcL1eM7oQ+H4FJHRa3DH8WU8Avil6l3wLTPbHY/MApxQmkKvNObnzOy3eKrGf4F7gXUrnPeqmV0OTDGz1yIP9+dmti5Rvgp33tqlbmdmD+EvBCuY2bN4BH2keVWGmyNNoA9wk/mCvl7Af3DHjLj/j4Angb9HxLKe0mgG/DacvE/gTt7RdbQdjEeG+5vZDnHsGkU1iRr8Gs+XPtnMinze4yS1tXDzd8AFEd0HGCPpjti+BH/5uTbGDR7JrZUH/Wl8NqI3nv7wGM0vATvhsxJTgabo82+SviWv4HAocGNcO5Hm34HipWBz/PckSZIkSbqUXnPnZuWvJEm6jqampgHAtIaGBvr06dPV5nQq3VWqt6mpqW654e5KTx9DT7cfev4Y0v6upxjDrFmzmDp1KsDqjY2N0zvSV90lnZIkSZLKNDU11XVed3RukyRJFkbqlertheeG7gOsKGm9WMm9sqTWeXhdgpmNpHK5sBGligtdjplN4OPPfbykmrm18+G+59Oc11swWx+vdzs/7jUOX4RUZoak3eb3vZKkO9BW1OT9OXNYoneqmidJkiwo6g0nnAx8Ba/xWdRafRbPD+wWDq6kC4ELu9qOtugMh7LO+3aqA93qXunIJu3m4FvG8vrsD7rajE7hxqH7dbUJSZIkixT1piiMAHaRdBXNkqfTgC90hlFJkiRJkiRJ0lHqjeD2xgvoQ7ODu2zpWLIIY2Y/wytJDFRJyraO67bHVeQGAmeXSmO1Pq8PMBbYEEAh6VxqPwFf/f8B8BZeZ7Yo8zYAOA+vNTsHF2a4yMyGAj8pdbMqcJ+kr7fRVtWWuNfTeBWCghZVDaLMWBPwXjmaH+Ihv8GlqAH2lTQ5au7+AZf97RV9j4yqBiPwWZXpcc1H1SNqPVsz+ylehWJOPLMTimoOZvZj4JvR1gs4VVILUY0ogXYXcIykQlTjXLyE2YfR5/GS7iJJkiRJuoB6HdxbgDPN7LvwUU7uL/B6oMkiRqtauBvgub3/qn1VRf6J53YPpbbAxBzgDLxM2p2tbBkMHAasI+kdMzsaVxTbOb6nfwJOknRD7H8KQNK1wLWlfiYStWNrtdWyJXg9SptV45e4it+gUv+r4PVjd5CkKFG3eDQfiperGxj71+CKZYUgxZ2Shla4T61n+w/c0X/XXCb4XjP7jKT3gHOKkmdRqu0JM7td0mtxrC9e8qx1PecTFHLI0eddZvapVrV0kyRJkmSBUK+D+128APwb+B/et3FRhEqKX0mdhPDAj3EFqP7ADyRdF5HACUV0sLxfbONRvR3xiN9+eOH9jYH3gN0lvVDlnsOAb5QifYvhKlWb41H584BlcKfoAkm/j/NG4bV1DegLDI5o5rn44sN72jv+EPXAzL7WxnmzgTtj7K2Zi38nlwbeAZbD88MBtgPeknRD9DMXeLF1B+Gkr0pJ0ataWxu21MTMtsTV1s6k5ODidWjHFMpu4Wi+Vxrf0jQ7vMuUxleVWs+2Ve3dR/BIbX9cAvuNUtuycf9yKtOZ+AvELq36LF+3HM0zPUmSJEmywGkzBzeKug/F1cVWw6N1X5S0h6S3Otm+RYE3JQ0B9gfOqvOa/sD9ktYHLsKni88NJa0mKleTKLge2NLMiqn1nYAnJE3Dp7q3k7QBLgd7qJmtXbp2MLBjKUJ5MnBZWUVrQSNpMu50TTezQgDiR9G8DvCKmV1jZhPj389V6OYg4HJJ77ezrRL9zGyCmTWZ2Q8iakwoiP0ej762Zh2gr5ndHXaeGS8PAP+Hp138N37eCKWygq3MbJKZ3WdmX63TxjIHAM9I+shpNrPDzewJXMzh0CLFwsx2ApaLCPfHMLOTzewZ4AZgz4zeJkmSJF1Fmw6upDnAmZJmSnpR0kPVooNJh7gq/h0PfDZyNNvibUmF2tvDeOStKIXWBKxR7UJJ7+IOSCGHPAKPzoNHCi8ysym4gttnaRlpvFbSOwBmtimeh3peHfZ2GuZyxLsDa0haBR/L6GjuDWwL/DReBh4otRXX98GfxcckZmu1VeF5YNXIrd0Jl/g9ONpOx19C/lPhut54BH0PPAq/GnBctG0X/34mfpYwsyKf9kZgtXjhOAb/7MovJDUxs63wVKMW8sySzpe0Fv4y+2Mz6x+KfKdR4+VJ0omSvoi/EP8mlOeSJEmSZIFTbxWFP5vZrp1qyaLLTPjoRQI8bWQ2LT+b1k7vrNL2nKKP0n5bqSejgOFm1h/YiuZ8018BLwDrSxqE52qW711eVLgVsDYwzcym49P4t8Xipo8RTtKk+Lm60jkdZE9giqTnY38MsE1szwCaJD0R+5fhkekyewD/lPRIhb5rtX0MSbMkvRjbLwKX444rwBbAifGsrgIGmlnR7wzgRklvRKT4jyU7DweujxfMmcDVxfgkvRzpDIQc898qjK8i8YJyGfC1IjWiwnimAM/h0sQNuIP9jxjDUOAkMzuxwnV3A/1ozhtOkiRJkgVKvTm4S+J69w8A/6aUXycp83DnPy8Ai5vZGpFLuW9bF7QHSfebWT/gVOCGiOoCLA88Imm2mTXgq+KvqNLHaXhED4BwenapVkUhprlrLb7qKNOAA8xsmYgu70xzFYNbgFNjAdXzeM7y5FbXH0T1CG2tto9hZisBr0n6IKof7IZHWYn0keK8rYEzSlUUrgB+ZWan4RUIdijZOQ3YwcyuwXNldyzGZ2arFBHhiGRvApxSh51DcEd5qKSHW7WtI+mx2F4dWB94TNLjwEql80bheeHnRBqGFS8SZrZhnPvPNh9akiRJknQC9Tq4U2lZ+ijpRMLBPAa4w8xeAm5q65oOMBqfnt6ydOwU4FIzOxh4ErivE+77EWa2BR7N7Af0MrO9gYMl3WZmhwOflXRinPsQHiVewcyeBW6VNBLPKd4YaDKzWcDrwIEAUVXhKOCWcMJewVMyivt/Do+w7lXBtlpt1WzZAjjZzObgi8JuBM5p6zlI+ruZ3QJMwiPwD+PRdICTgAto/v2bjFdiAPiWme2OR/zBKxlMbOvZ4mklSwH/Z2aFGftHxPbnZrYu7mjPAY4O57YWvYALzOyTYct7wF5F5YUkSZIkWdD0mjs314EkSdJ1NDU1DQCmNTQ00KdPn7ZO75F0d6nepqamNuWGuzs9fQw93X7o+WNI+7ueYgyzZs1i6tSpAKs3NjZO70hfdUVwzYvQVyTy7ZIkSRZZ2vrD0p2d2yRJkoWRelMULmq1/ym8+PyzpFxvt8TMRlJ5xfuIUsWFzrjvYJqrMpQ5R9KFnXXfJOlKys5td4/WJkmSLArU5eBKWr28H7Vxf4LX50y6IeFMLnCHMpznzlhMlizkHHzLTbw+e3bbJ3Zzbhz6sbTtJEmSZAFTb5mwFkRJq18CP5y/5iRJkiRJkiTJvFFvikIlvgJ8OL8MSZKkOmY2Cdi0qHvbjuumU6N82/zCzEbgSm3T49C0Qg46SZIkSRY09S4ya1H7Fle8WhL4VmcYlSRJS0ryyN0OMyv+H7lT0tAuNSZJkiRJqD+CO6zV/jvAk5LenM/2JElSATObC/SV9HZEZcfgsyifwUUjzonztqRZPvlevEZtrX6fwgUfJsf+t4FGSQea2Rm4Yt0SwMvAQZL+ZWYDgAn4YsJt8Tq9Myt0nyRJkiRdQr05uEMk3Vv6mSDpTTP7XqdalyRJNZaWtCkuo3uamS1rZn1wcYejJA3EhTpWa6Of0cDw0v6BwCWxfZqkISHbfCXw69J5/YGHJG0g6fw4tlVIMd9nZl+dp9ElSZIkyTxQr4P7Mb354Cfzy5AkSdrFVQCSpgOv4cpqBrwr6Z5o+yPwRhv9jAH2MbPFzGwgLtf812jbyczGm9lU4FhaVseYCfyxtH8jsFqkUhwDXGRma3d8eEmSJEnScWqmKJQEHnqb2Ta0nO78AlkmLEm6inJKwByq/y7XlCqUNMPMHgV2wqPBoyTNNbPPA7/DZ2+mmdlmwBWlS9+RNLfUz8ul7Ylm9jdgI6Atmd8kSZIkme+0lYNbCDwsCVxcOj4XeAE4qjOMSpKkQwhYysy2lPRXMxuKR2TbYhQwEhgCbBLH+gHvAy+Y2SeAw2t1YGarSPpPbH8++jmlQ6NIkiRJknmkpoNbCDyY2RhJBywYk5Ik6QiSZpnZPsB5sSjtPmBGHZdeD5yL59TOiL6mmNk1wGP4ArObgS/V6ONbZrY7UCg1nCBpYgeHkiRJkiTzRL1KZuncJkkXIqlXaXtAq7YBpe2/AgNLzZXkmlv3/S6wXIXjx+D5tAU/i+PTgRVbnXsCcEJb96rFRTt9lT59+sxLF92ClOpNkiTpeuqtg9sP+DleMmhFSrm4ktpapZ0kSbJQ09TURGNjI0A6t0mSJN2Aeuvgnoev0j4ZuAyvi/sD4LpOsitJkvmImU3g47/v4yXVzK1NkiRJkp5IvQ7u9sDakl4xszmSxsYfzD/jK62TJOnGSNqwq21YmGlsbMzUhCRJkm5EvQ7uJ2iup/m2mS0HPA+s0SlWJUmyyDHyltt5ffacrjajw/x56Ne62oQkSZIkqNfBnYzn396FF4E/D3gbeLKT7EoWMszs58Cyko6t0DYC2EXS0AVtVzXK9prZ4cBSkrp0tsLMTsDTgz7Aa1AfJunRGuffg8v43tjJdvUHLgW+iJcWeypse6kz75skSZIk1ahXyewQYHpsHwO8h9fXzOoKPQgzy/nTDiDp/K50bkNlbDBwGC68MAhXETu9q2xqxVzgN5IsJIKfAU7rYpuSJEmSRZh6y4T9s7T9Il4UvsdjZpfj8qZ9gKeBg4BrgLMljY1zdgG+L2kbM1sHuARYBpiEp2icUi1CZmZbA78HHgQ2xR2BvSU9Hu3HAfvH6Q8BR0l6u0pfOwO/Kh1aB9gz8qGHA0fin+cbwBGSFJHRYXi0b01gmJmtDJwK9AZewiNtT5uZ4QX/l462UZLOqGLL0sBoYF08mihJe0XfV+IiAUsCN0n6YYXrlwDOBrbFa6xOLLX1Bn4N7BiHbgWOk1Rx7trMBgATgD/ENUsB++HCBBvjL2O7S3ohzj8O+EY8q/8Ah0h6IdJuLgIacBGTfwP/jWt+TnM0t732XQhMkfS/sd8AjMOjnfvgL4xLxOnHSrorzpuOy/FuC0wBzgIWxz+fd/CyXs9WumcrtjKz44HPAn+UdHz0/31g73gOM/HvzKRo2xR3nvtGHz+QdHt8R36PV1JZAvi9pEskvQrcU7rneOCIOmxLkiRJkk6hrgiumfUys0PM7G4zeySOfcnM9upc8zqdYyRtGFGnR4HjcCdveOmcA3GnFnwa9mxJDfgf+iF13GNd4HxJ6+FRt58AmNlOuHO7GV63tDfw02qdSLpZ0mBJg/Gi/BOA28xsS2Av4EuSGnHHpKw6twnuODUAz8UY9gt7rgAuj/OOBMZJGhTnXkR1dgD6SVonoomHxfHXgV3DjsHAhma2Y4XrDwNWx530L+OSrgWHxrUbxM/6cawW/YH7Ja0fdt8FnBtjbCJqwZrZMNyx3ETSBrh4wW+jjxOBNyWtBQzFU3Iq0V77RvHx79OokLm9LWxZH3c2R7e6tp+kjSQdLGkycCYw3cz+E+f/qMZ9C1bDBRrWB0aa2ZpxfIykIXHvnwLnA5jZJ4E/AT+Mz3YD4CEzWwz/vnxX0hBgC+B4M1urfLNQPTsCd+KTJEmSpEuoN0XhZOBg4AL8DyZ49Oi4zjBqAXKAmTWZ2RRgX9xxuR7Y0sz6R27hVsB1UQu4Af8jj6QJwCN13EMlRafxuIMFsB1wlaQ3w9m5II7VxMx2AL4H7CZpJrArMAh40Mwm4VPDnytdcr+kZ2J7Y2CypMdi/xJgsJn1xVWvRprZL8xsW9xZrcZkYG0zO9fM9gRmxfHewOlmNhl3LBvwZ9qabYDRkj4IkYHLSm3b4Q7g+5LeDxvbei5vS7opth8Gni2ikWFHsRhyt+jr4XhW3wIGlGy6CEDSy/j3oBLtsk/S/UBfMxsYTuI+NDuyX8RfUh4FrgZWjih4wZhiI+RvdwfWkLQK7ji3dogrcY2kDyW9ATxO8/ev0czuM7OpuONcfE6bAo9J+nvYP0fSa8D/AGsDV8Wz+ys+s5qhBAAAIABJREFU87F2q/udjefnn1OHbUmSJEnSKdS7yGwEsL6kl83s/8WxacAXOsWqBUBEPo8ANpP0kpntCxwq6V0zG4s7vABjJb0TDi54mkF7mFnankP9z7ySzYPwSNsO4YSBi25cLOnEKpdVTHlojaTrzOwBvCTc8Xi6xrAq5/7TzNbFo687Ab8ys4G4470CsLGkmWZ2AZ6q0NnMKm3Pofoz74WnlJQj3AuC0fjv0D3A45L+FcevxNNfbojI57u0fF7lz25PPNXh+dgfg4uvtMXHnkWkiFyLR/0fNrPP4ukategFvBwzCBUxszPwVJhdJX1Yh21JkiRJ0inUG8HtTfMf28LBW5Y6naduyvJ4vuorZtYHd+gKRuEOyQgiPUHSm3gawz4AZrYBLSVR28udwDfNrK+Z9cLzmu+odrKZrYILawyTVK5e8Wc8Er1qnNfbzBqrdDMeGFSaVh4OTJT0lpmtAbwgaRRwEi3TBlrbsiowR9INwHeBTwGfxJ/p8+HcroJHHCtxN7B/LJ5aiuaXCfDnMtzMFjezxcPGqs+lnYwDjjSzFWIcfeKlobDpwDjeH9ijSh8dsW8M/r0ZSXO6C/jzmhbbB+ER0WpMA7Yws2Vif2dgahv3rcaSuNP/79g/stT2ALBO5OEW36cVAAHvmlmRM46ZrVW8+JnZr4BG4GuSyi8cSZIkSbLAqdfBvRk4MxxBwiH7Be5c9VRuxVd7Pwnci09tAx9NK/fDcyDvL11zAPCdSGk4Fl/88wYdQNIt+NT8A9EPwCk1LhmJO5Lnmtmk+NlG0n3Aj4FxkRowlSqOZZRt2h+4InKph9Ecpd0LmGJmE/Fp5mNq2DIQeCDu9w/gVEnP4QuhNo9p7yIXthIXADPwKfO78QV25bZH8IVnE2P7DzVsqRtJl+I5x/fG+JuAzaP5F8AKZvYE/iJxXw3b22WfpBnAY8DWtEx9+A5wg5k9jM+GvFKjm+vx72xTPPcRhEPeXuJl7UQ8t7YJX7RWtL0KfB3/fS+eUaOk2Xg6zN5m9kikVZwHLBHR/B/hC9n+Ht/NP3XEtiRJkiSZH/SaO7ftGfeI0ozGp6MXx6c9bwcOkPRWp1rYjTCzZYF3JM01r6hwD2CRo5gkSQdoamoaAExraGigT59aQezuTU9WMmtqaqKxsdrET8+gp4+hp9sPPX8MaX/XU4xh1qxZTJ06FWD1xsbG6R3pq2Y+qJmtLOmFiPjsYWYrAZ8H/l2UXVrE2AxfRNUr9g9J5zZJkoXhD0uSJMnCRFsLnp7Ep+oLzpf09U60p1sj6XY8ct0CMxtHc3WJghmSdmvvPczsRHyKuDXbRw3iBUY3s+V8vORZmdmSNlyQdlSjq+yzj9dHLjhB0s2dee8kSZIk6a605eD2arW/dSfZ0aPpiCNbo6+T8bJsXU43s+XwrrahFl1lXzix6ch2MWutu25Xm5AkSZKUaMvBbW9JrCRJkg5xyC1/4Y3ZPbO62NihO3e1CUmSJEmJthzcxcxsG5ojua33kXR3ZxmXLLqYywzvImlohbatgTO6S3oCtLTXzHYDtpT0gy62aS5eoaPwGveXNKXGJbX6mo6Pb2qM9e9FuTozG4yr530CX4T6N1x2OsuFJUmSJF1CWw7ui7SUfX2l1f5cerDYQ0/BzHpLmtPVdiT1IWkcXShVa2aLRVkvcCGT+V2vegTwMp6jD14jdxNJ74dgxTW4HPNZ8/m+SZIkSVIXNR1cSQMWkB3twswuBwwvjP80XiT/GuBsSWPjnF1wlahtoqTXJcAywCRcuvUUSTdW6X9r4PfAg7h06Vxgb0mPR/txeD1Z8BquR1VzIiosAloH2FPSWDMbjhfZXwyvp3uEJEWEbBjwFq4MNSwkXE/FRTdeAg6T9LSZGS5MsXS0jZJ0Ro1nNxevm7sH0B/4QaiYDQAmSFoxzvtov9jG673uCCwF7Accjsv/vgfsXq2yRjg95wDb4qpjb0vaPKRrbwo7lsJr6h4WErit+zgF2Bt4DS/PVm6r+/MoPYOfAF+Lex+Cy+3uiEcg9yx91tU+oyXwesHb4s7exFL/IyhFn9v5ffkJ0F/Sd2O/P+5Afh5fxHYKzUINv5R0VZx3D/7d3gR4FReCaBflKG2l/Th2ILAhcFZ8JsdKurPUzeL4Z9kzcw2SJEmShYJ6hR66G8dI2lDSQFxd7DjcyRteOudAmlWjLsWd3wbccR1Sxz3WxatGrAf8EXeIMLOdcGdlM1zwoDfw02qdSLpZ0uCQOD0XdxRvC6ngvXC51EbgdFpGxzfBnYcG4LkYw35hzxW4YAG48zVO0qA496I6xvampCExjnqjbP2B+yWtT7OIw7lhTxPw7RrXDgK2AdaRNAjYJY7PAfaNVIMG/Fke1PpiM9sV2A0YjD+XtUpt7fo8Srwez+A4YCzwtxjbGPwFgDY+o8OA1fEXli9TRfmtA/aNwcUUipfPffHP9x1cjGSLsHM74IxClS34QrSXndt7Qnjh1EKoZV6QdAn+HT46vtd3xjg/a2aTcGf/LVwQI0mSJEm6hJ7q4B5gZk2hKLYv7vhcD2xpZv0j6rUVcF2IVDTgTiGSJuDqU20hSUVUbjzwxdjeDrhK0puS5uJ/yLdrqzMz2wH4HrCbpJm4KtQg4MFwDE4DPle65H5Jz8T2xsBkSY/F/iXAYDPriytujTSzX5jZtsDrdYztqtK4PmtmS9ZxzduSborth4FnJU2K/SY8Kl6Nf+KRvYvKUq/49+/YGP8jeDR0cIXrtwGulvR2pGqUnfgOfR7A1aWxzC1F88tjqfUZbQOMlvSBpHdxVbpKtMu+UD17lOYI7Aj85Q1cye7aUIq7DZdHttLlV5RSEwBWi5eHL+GOeD2Of4eQ9Fy8xK0MLEHl8nJJkiRJskDocQ5uRNWOAHaMCO5PgCXDyRiLO7z7AmMj6lXQ3ooQM0vbc2g7X7mWzYOA8/Fp/JfjcC/g4iK6GxHYci3duvImJV0HbInLDh+PR3rbYmZcW+T1LgbMpuX3obXTW14wNId2PB9Jb+AR8auA9YBHI+ViX2ALfEHWQFz6tR5ne35Q2D+Hj4+tGEtbn1FnMQoYbmYDgeWAv8bx/4enZwwMZ/JZWj6vFt8ZSf+Of98ELqRZlrgabX0H2iR+567GU1iSJEmSpEvocQ4usDyeC/lKTLmWp7RH4RGvEUR6QvxxfxTYB8DMNsCnijvKncA3zaxvKJqNBO6odrKZrQJcBwwrVp0Hf8Yj0avGeb3NrJoU0nhgkJkVU/PDgYmS3jKzNYAXJI0CTqLKVHkdvAAsHv2BO5/zBTP7FLC0pNtwJ/wNfDp9eeDlGMdyNe55N7CXmS1jZr3x9JOCdn0e7aTWZ3Q3sL+ZLWZmS9WwvSP2XY9HXb+P51QXL2fLA9PlUtFfoUbU3MxWCLuIdIeheI5uLZ4m0nfM7MvAp6uc9ybueBf3+kKR/hC5ybvj1RuSJEmSpEvoiQ7urXi08kngXnyKGQBJ9+PKa/1iu+AA4DuR0nAs/sf3jY7cXNIt+HT0AzT/ET+lxiUj8anlcyMXcpKZbSPpPjzXc5yZTQam4o5BpXu+hOdxXmFmj+AL0IZF817AFDObiC96OqaD45od195hZv/AI5nzi88Bd8Y4HwFuwZ32MUBfM3sCdyb/WuniSB+4EZgc1z1Zamvv51E3bXxGFwAzgMdxZ/ehKn20277SbMT++DMqOB7Pu52Ef+61Um3WwlMrimf+AW2nKPwU+H70/1V8fJW4ADgxvsvb4fnFE+JeD+OL3H7Rxr2SJEmSpNPoNXfuwq/lYGbLAu9E5GsdfJrXJL3WtZYlSdLU1DQAmNbQ0ECfPvO8Dq5LeGfmTJZZckFl18x/mpqaaGysNoHUM+jpY+jp9kPPH0Pa3/UUY5g1axZTp04FWL2xsXF6R/rqiRHcjrAZMCmin1cBh6RzmyTJ/OKJRx/tahOSJEmSEh1eONWTkHQ7cHvr42Y2Dmi9aGiGpN3aew8zO5HKK8e3l/Rie/ubF7rKFjMbSeVyYSNKFRcWCN3p86hEF35GresyF5wg6ebOum+SJEmSLEgWCQe3Gh1xZGv0dTJw8vzqb17oKlskXYiv1u9yutPnUYku/IxuBtKRnQfenzOHJXr37mozkiRJkhos0g5ukiTdh0Nv+RtvzO7+Amg3DK2nzHKSJEnSlaSDm7SLkEneBvgM0LcsORsSuFNolmndX1LVclFmNgz4IS5C8B1J51Q5b2s86lhUT5glaeNoOwQ4Cq9ZOxf4jaTLom0MXne3YD3ga5LGmdn2+FT9QFzl7tjS/WpdtxJegu5zuHjFX3BVr9lm9lNcTngOXrXghCiNRkgq/z9gxejz+5LuiLalo89GvBbtsWolIx3P4C5cxe+cOHYsLjO8Ji4gcmMc/wQuXd2A1/t9ETi8EA4xszOAbwAD8Jq6ZSneWm034OptH+I1d49qnXpiZj8Dft762iRJkiRZkCwqi8ySeaQkHXsRldXGCjYrCSO0VQt1Eu4QXlGHCY+V+t24dPwpYOsQitgZ+L2ZDQCQdICaZZKHA6/hCmDg6mojcfndFrRx3QnA4yFRvB7ulBa5tP8AhkTbQcDVRS1a3IG9JNq+AVwSji146bo3Ja2Bq6ddGJU/AAjFul/j5dXK3Btjvq/C8xoNrC2XRh5LS+ncG/A6u/+qcF2ttuEhdrE+cAYtpaWLGtObVLk2SZIkSRYYGcFdRDCznwD9JX039vsDwh24n+CqVYsBv5R0VZxzD+6EboLXNt1Z0t3RNs82FRE+M+vwvLSke0rbz5rZ88CqwPRWpx4MXC5pVpz7dNz7a23cosV1eJS4b0RJ++CytP+JPm8rXfcIHlXujyuODcJrOCPpKTN7FdgJFwH5Jv45FG0Tou2a6OtM3BHfpdXYH4oxtH4mHwLjSoceAL5Tar+/0nV1tJVrRy9Hc6SeEHo4FxdUuedjFydJkiTJAiQjuIsOY4C9S5HYfXEn6O/AFhGV2w4XElihdN0Xon3nOu9zTwgAnFqoW80n/sfMHjazB81seKUTYhp/eaCp1fEl8PFeXOGyqlS57hfA/wDP4+pvt0n6W4XLDwCekfRs7DdFX5jZhoABn4+21WgZ9ZyBp0BgZjsBy0m6tj22t+LbtHR4O4yZXWhmM4BfEk55cDJwmaTp8+M+SZIkSTIvpIO7iCBpBi5ZXDiqI3Bp408B15rZVHwa/pO481VwRaic1cNqkjbEp7jXoW3lrHp5GPicpA3wlIYTQ0HrI0LAYwywj6T3Wl3/Nbz8W3tLlVW6bk88OvsZYBXgS2Y2tJUtW+GO8D6lwyOAbUMl7HvA/Xi+bVXMbHngNCqXXqsLM/shsDYepZ9nJI2UtBqeqnF63GNTYEPgvPlxjyRJkiSZV9LBXbQYBQw3s4H4FPNf8YVP9+CLggbj0+llSaa3qRNJ/45/38TLhW0+P4yW9GYxPS5pGp4n+lHfZrYmvgjtsFYSzQUH0c7obY3rjsJTFj4Mm8bii+4KWzbFpXm/JkmlMfxT0u6R27sv7iA/Fs0zaI7mgkd0/40vEvsM8A8zmw4MBU6KGrptYmZH4VHjnUP+d74h6VJgm0h12Qp3oqeFnasCt8VCviRJkiRZ4GQO7qLF9cDvgO8Do0K6eHlgemx/BVijIx1HWsNMSe9FGsRQPH93njGzzwAvhI2fBLYnIpJm9gU88ny0pNaLsDCzVYEtaRlNreee1a6bBuyIO51L4Gkd18c1Q4CrgaGSHm7V30rASzGGEcAsvCoCeK7tYcCEcNaH4JHot4CVSn2MAiZUqzbR6n6HAYcC20p6tR1Dr9bfssAKxUuMme2K52W/Kuk0PNJcnDsd2CWrKCRJkiRdRTq4ixCS3jWzscCBeLkngOOB88zsJOAhfPq9KmZ2PbBR0aWZTZW0A7AW8H9RKmxxPLe3ZoqCme2DT3OvAOxuZsfjSl6PmdnJwHOSzserDhxhZh/g39nRksZGN7/GF3KdHNcAHFda8DUc+HNraWYz2wKXbe4H9DKzvYGD27oOX6x1vplNAXrjZcL+EG3nAUvFcyjOL0ql7QYcF8/nGWAPSXPjnNOBUWb2NF5i7NBwbmtiZj8AjsHTTEaZ2Uw8NWQuHpn/F3BH2FIurXYWXvlhZeBOM3tF0rpttC0DXGNmy4SNrwK7lsaQJEmSJN2GXnPn5t+nJEm6jqampgHAtIaGBvr0mZ/rEjuHSkpmTU1NNDY2dpFF805Ptx96/hh6uv3Q88eQ9nc9xRhmzZrF1KlTAVZvbGyc3pG+Mgc3SZKkHaRMb5IkSfcnUxSSTsXMdsYVw1pzgqSbF7Q9SZIkSZIs/KSDm3Qq4cSmI5v0eN6f8yFL9M5JryRJkp5AOrhJknQLDrt1Am/M7r5rAv70jS262oQkSZKkTtLBTWoSwgSbVhBPWOgxsx3xKg1FZYgbgJ9Eqa9l8aoJ60fbhZLO6DJjOxkz+zmwrKRjzWww8D+S/lhqPwOvdjEAr6mcJcKSJEmSLiPn2xZCzGy+rYIJYYJF0bldDFcb2yAEMAYDXwF2jVNOAN4H1gMagf3NbJOusLU9mNknzKzXPHYzGNir1bEbcAW7f3389CRJkiRZsCwSEVwzuxyXn+0DPI0rVF0DnF3UUzWzXYDvS9omZF8vwWt/TsLFD06RdGOV/rcGfg88CGyKR/z2lvR4tB8H7B+nPwQcJamiQliFRVnrAHtKGmtmw4Ej8c/tDeAISQrhgGHAW8CawDAzWxk4Fa/V+hKu8vW0eVHUUcDS0TaqVuQx6rb2lfR2FPC/DPgyLlN7PC5EsC8u8XuQpPvMbAAwARiNO4W9gCMl/bXGfT4NnA98Mc4/XdKYaJuO16z9Cq7A9vtaYgdm9hQutjA59r8NNEo6MCKNWwFLAC+Hzf8q2TwK2Ba4IGrwFiwZ13wY+4Pi2c0F3jGze4H9gPE17NoUr3nbNw79QNLtIRBxFv59ewcXrXioZNMfcHGJpeIehwMbA+8Bu0t6Ifo/Do+iLgb8BzhE0gsRfV03nt1q+He0dX3fFlHaSvtxrD9wMtAvovv3STq6UJAr1f9NkiRJki5jUYngHiNpQ0kDgUeB4wjZ2tI5B+JOLcCluPPbgDuuQ+q4x7rA+ZLWA/5Is9LWTrhzuxkwEHcqqwogSLo5oqaDgXNxB+c2M9sSj5p9SVIj7iiVZWQ3AY4Nm5+LMewX9lwBXB7nHQmMkzQozr2ojrGV6SNpU9yR+gPwgaSN8Ihm2THvD0yO+x8FXGlmtYqcngVMjfO3B04zs4ZS+0ox7s2BE8xsvRp9jab6Z3uapCGSBgFX4ikIZZsfkrRB4dya2YZm9gjwInA3cFOc2wQMNbPFzWxFYAdayu22IBTY/gT8MO69AfBQqKFdh6c+rId/N66L44VN90taH/+s7gLOjXObgG9H/8Pwl4NNJG2AL+z7bcmEjYF9Ja1VQbyibiS9ApwI3Bnf06M72leSJEmSdBaLioN7gJk1hfrUvvgU6/XAlmbWP6JSW+GORT+gAXcKkTSBNtS9AkmaGNvjcWcDXMr1KklvRrTvgjhWEzPbAfgesJukmfjU+CDgwYicnQZ8rnTJ/ZKeie2Ncefysdi/BBhsZn2B+4CRZvYLM9sWeL2OsZW5Ov59GI8CF/tNtJT5fR+P9iLpHjzaWCu8tx3wf3H+87iDtk2p/aJo+y/uZG5do68xwD5mtpiZDQSWB4ro8U5mNt7MpgLH4t+Fgpn4y8lHSJoQzuTn8FSELaPpNDwCPAGPLt8DzK5h06bAY5L+Hv3OCUfTgPcl3RXH78SfXfGs3pZUONUPA89KKiSQy898N/wZPhzfj2/h+bAFN0t6uYZ9SZIkSbLQsNCnKETk8whgM0kvmdm+uBRqIVu7b5w6VtI74eCCpxm0h5ml7TnMw7M1s0H4dP0OJaekF3CxpBOrXFYx5aE1kq4zswfwKOnxeLrGsHaYNzP6mRPT0cW452nM8xNJM8zsUWAn3BEeFQvDPg/8DhgiaZqZbUa8yATvVJOelfSymd0C7IlPy7+LO5EAmNl5wGOVrp1HZpW251D9e9YLT6MpR/XL1PP9mE3Ll94l6zUySZIkSboTi0IEd3k8X/WVmCI/qNQ2ChgRP5cASHoTT2PYB8DMNsBTCzrKncA3zaxvLO4ZCdxR7WQzWwWfsh4m6clS05/xSPSqcV5vM6umyTceGGRma8X+cGCipLfMbA3gBUmjgJOAjeZhbLVYgnh5iJeMpYAnapx/J3BInL8ysDOeElAwIto+FW1/aeP+o/BnvQ+esgDQD4+OvmBmn8BzWatiZv8T52Fmy+AO85TY72dmS8X2esAeeFWFajwArBN5uMXntwIgYAkz2yaOb4tXZVAb42vNOODI6BMz6xMvSu3haaAxFqL1BXapct6beD5vkiRJknRLFgUH91bgGeBJ4F58mheAWBjTD+hXLJIJDgC+EykNx+JOzRsdubmkW/Cp+geiH4BTalwyEvgUcK6ZTYqfbSTdB/wYGGdmk4GpwO5V7vkSnvd7ReSPDqM5SrsXMMXMJgJnA8d0ZFx18AqeFvEI7vjtI+n9GucfjTvlj+AvAMdLerTU/rKZNeHP8VRJUyp1UuJ6PHr7mKQZAHHNNXik9UFgWht97I4/q8lx/n3AhdH2BWCymT2GO9P7SXquWkeSXgW+DpwZY2zCF769j+cz/yqO/xJfIFfrWVXq/1I8z/reUv+bt6cP/Jm9Cjwe201VzrsLWMbMJpvZWQBmdpaZPQusCtwZEfQkSZIk6RJ6zZ3bfQurdxXmNU7fiWntdfD8SpuXxTmLEsXqf0krzqf+pgO7ZG3VhZOmpqYBwLSGhgb69Km1DrFrqaVk1tTURGNjtQmV7k9Ptx96/hh6uv3Q88eQ9nc9xRhmzZrF1KlTAVZvbGyc3pG+ukXOZDdkM+D0Ur3QQ9K5TZJFm5TpTZIk6Tmkg1sBSbcDt7c+bmbj8DqiZWZI2q299zCzE/Ep69ZsL+nF9vY3L8xvWyRNBz4WvTVXwBpV4ZJzJF1Y4XjR34BKx81sAh//Do+XVDO3tjPpTp9rNbrjc0uSJEmS+Uk6uO2gI45sjb5OxgvmdzkLypYobzW4zRPr72/D+dXX/KI7fa7V6I7PrTtTKzUhSZIk6Z6kg/v/2Tv3OK3qav+/ERRvGEVaeUtTW4YjoCNKliZmif68ZOUNUdBDeSnznp70dEpNPck5x6PSIa/jNbxgQl7T+qlZoTIKgtbHNDke80epqagJCs7vj7W2bMbneebCzDwzuN6vly/23t/73htcz9rruz5JkvQKjr5rLq/VyiRcJ6Z9LX8PJEmS9DXSwE26lBAZ+Kykt+o9lxXFXIJ5tQhZ6YnxxgHfxeWZjy/LEZvZ6cCBeO7bfngmiRsqdsR7c5/UE95aMxuDK8K14CnObsWV2XIHa5IkSVIX8rtbgpn176q+Qr51ZTBuB+Bpxr7cQ+P1B2YDB7G8+ETBxZKGhWTvnsClRc7bXsCDwLYhLz0C+BKuvJckSZIkdSE9uBUws+twqdSBePL7I/D8qRdJmh519gJOkjQ6UoldCayFGymb46pSt1XpfxfgAjy36mdxz9dBkv4Q5afieWwBHgGOlVRRicrM9gTOKV0aCuwvabqZjQeOwZ/za8DRkmRmE/C8uK8DWwDjQlzhXKA/8CJwpKSnzeXKmnBZ3v64KtikGveuBRgk6Y1I73Ut8EVgA1w5bT1cAOIjwBGSHijSiuGCDF/CPZTHSPrN+wZYNs5YPIfvanHp5ELuNsadGn19CLig8Iaa2UjgQvxZvQl8R9IjpTk0Abviz/MoYBUzK+SWz6swj42Bh4GNJL0T127GhTmuw2WFh+BCFw/HfX270jMoJHjN7N3W40gq52FeG39n2vqBOsDMfkqrdyye9c/wHNCrA7dL+m6MvRr+Po3BvcV/lrRflJ2K5+wdAPwFzy6yoNW7uTr+TN63hiRJkiTpKdKDW5njJG0naWtc1exU3PAZX6pzOKF+BlyDG78NuOE6sh1jbAVMkTQMuBE4A8DM9sCN2x1xBbX+wL9U60TSHeE1HQFMxo20u0M97ABgZ0mNwPlAWcZ1FG4UNgAvxBoOiflcjxtn4AbyDEnDo+7l7VhbmYGSPosbRpcC70jaHvgeyxvmQ4A5Mf6xwM9Cea4adwOjwqN5EMvUygrWi3V/DviemQ0L420a/vl8GH5fp8X1Yg6PSNpW0kW4XPLVcX/fZ9yCywLjoht7AJjZENzzezNuII6NMIEG/FmWlfTeewaFcVsLMzvKzP4IPIbLTb/cRpOK7xjwKrB33J8RwHYRZgDwz7iIxbaShrNMXW4csBl+z7cF7gD+vTS37UJg4m+4At3tba0nSZIkSbqLNHArc5iZNYeS2VjcCLgF2MnMhoQR8wXcOFoHN16uB5A0C3i8HWNI0mNxPBM3HgAKb+HCiGG8JK7VxMx2B04E9pG0CP9EPBx4KOJizwM2KjV5UNIzcbwDblw+GedX4ipkg3D1rolmdlbIyL7ajrWVKeJEH8W9wMV5M+7pLngb9/Yi6T7gLdyLXo3NcEP+iejz4+GZLLg8+vorbmztEv29XXh6Jd0b4xbjLMINwY7SREgJ4+/LDElv4n+/To77/zjuGS5nkSg/gzaRNEXSlrhhfHq8h200qfiO9cfzPM/Bn0NDaV574R7vt6ODl+L6Pvh7+Gis51vAJqWBZoUhvRHQCOzU3nUlSZIkSVeTBm4rwvN5NDAmPLhnAKtL+gcwHTdgxgLTw4gp6OiGmkWl46WsQLiImQ3HvY37lgySfsAVhXc3PLDlHL4VQx5aI2kabqw8g4cYXNPB6S2KfpaWz1nBNeOf2H8iaStgW2AJ/nl8RXizkxuj3vvxgxu6hWd/LPB5YKd4l37Sao7tegatkUsOv4Ab7bWo9o6dCHwY2CGM0ltp+971w8NuivepQdL7pIDj/bsT2L/NhSRJkiRJN5EG7vsZjMervhyfyMtBWBK6AAAgAElEQVSflJtwA2YCYcRIWoiHMRwMYGbb4qEFneVe4EAzGxRKahOBe6pVNrMN8M/u4yQ9VSr6Be6J3jDq9Tezahp+M4HhZrZlnI8HHpP0upltDiyQ1AT8ENh+BdZWi9Vwg7D4kbEG8Mca9QcDz8bxEXi8dJkJ0de6+Kas/wsIWM3MRkfZrviuf1UZYyEew1uT0o+fc4F1SrHDg4GX4j5+qFhfZ4g47+J4U2Ab4MnqLWoyGPh/khbF+7Nvqew24PgibMPMCsGOGcAxxcY2MxsYP6wws0+b2SpxvBYerjG3k3NLkiRJkhUmN5m9n7vwzT9PAS/hn+i3B5D0YIQkIOnBUpvDgCvM7J/x/7HPxY3kDiPpTjMbBvw+Ls0Czq7RZCKwLjDZ94MBcIKk/xuppWbEDv3V8I1yzRXGfNHMDgWuj+wBL+L3ADyO9xAzexv3Uh/XmXW1g5fxsIjv4t7Cg4vP5FU4HrjVzF7Bn1nreNSXzKwZN1DPDa8nZvY14MIwxN4Evh6bviqN8XP8R8JsqmwyK9EE/Ibl46WvBvaNuNm/Rfka1Tows4PxWOkPR7vTcAW0J4EfmNlWwDu4N/Y7xabETnAhcJOZzQOeB35VKjsPN9RnxzN/Gr9H14Sxe3/cq1Vwj/Qc3ECeYGZL8PCHnwNVlemSJEmSpLvp19KSqSpXFDNbm/i8HZ62+wCT9Ep9Z9Y3KDIYSHqfvG8n+5sP7CVpXlf0l3Qvzc3NmwDPNjQ0MHBgrX2F9aE9SmbNzc00Nlb7QNL76evzh76/hr4+f+j7a8j5159iDYsXL2bevHkAmzY2Ns7vTF/pwe0adsQ37fSL82+kcZskKwcp05skSdL3SAO3C5ArXb1P7crMZgAbt7r8nKR9OjqGmX0f+GqFoi9L+ltH+1sRunoukuYD7/PemtkI/NN/ay6WVPUTuKRNOjqHtujsXLqbrnzHkiRJkmRlIQ3cbqQrjQxJZwJndlV/K0JPzSVyw45os2IP0JvmUiYN2e6nPSEKSZIkSe8iDdwkSXoF37rrTyxc0vsMyRu/NrTtSkmSJEmvIg3clZiQg70AmB+Xni1kVzvR1y7AJEnbmdlgXEnrx6Xy04ED8R3+/fDMBTdU7GxZm73xrAED8OwOh0fKrXKd1XCJ24I1caWt9ST93cxGAf+FpwlbFVeUmxJt78M/3y+Mtv8l6crIV3sNLnzwNvAnXEL3xWh3BHBCrGUJnpXiN1H2EVwxrhHPaHCDpDPNbEc8q0DBenh6tW2j3Sjgp3gWhfl4Wre/RdnhMV5/4M/AeEl/r9Uu0nL9Nu4HwP8DjpI0v1ZZZ/ustfYoa8GzhxQSvYcWmSuSJEmSpKfpfe6SpEuIdF8A95aS83fKuK3AYOC7ra5dLGlYSOfuCVxa5EytMr+1cenevSVtDrwOnNy6nqS3S/MfgRuRdxUGIC5wcVaUfRGYZGYfK3XxnVL7QoChBfixJAsBhmfw9FiF1O4FwG7R55m4MVjQBDwk6dMhMnFJzPN3reb5MKFuF4bjtcC3JH0aTz1XjPcZPA3cF6O/hwgJ41rtJL2Li5EMl0vq3gn8R1tlne2z1tpL7Fi6B2ncJkmSJHWjbh7c8PicDuwHDAFOkTStdcqo8nlxjBtGY3AP1CHAUbjc7Fu4mteCGuPOx/8H/0VgA1ydaz08Cf9HgCMkPRB194w5ro57+k6QNNM8EWgT7unqDzRJmhTexh/hMr4DcXnWoyW9YWbfxL10i/EfFgdIqihkYGZ/wnOPzonzbwONkg43s0nR/2p4nt4jJP1P6d404ZKwl7C8klWblL20lc5LTAYGR37Yf0jaUVI57+/auBFZ6wfUHvhz/VOcTwGuou3Y3sNxwYmCFpaJMQzCvbVvtm5UJozj+0qXZuLqdeDe537R119xY/55ADPbAhhGSRih0rtmZusBXwaOjEuNwKJS7uQpuOf0CFwmd3bhPQbuAO7H3+la7Wh1z9dhmfe0Vlmn+mzv2pMkSZKkN1BvD+5CSSOBQ/Hk8+1hCPBgeAovx5PUT5ZLjjYD325HHwMlfRb4Gm4svyNpe+B7LPOebYYn7d9DUiMuqHBjtD8GmBGeroaYB7hX8zVJ24cH7AXgn6PsfGDX8O6NBJ6rMb+rcDWxgsNZJv96nqSR0f/PgH9rdW8ekbRt8Zke+IKZzTazB8zs/7Tj3rSHbwGvhqdux+KimR0VogaP4SEMrcUXymwM/E/p/Dlgo1qDmtl2wCdwlbaCw4FzzOy5GPcYSWUJ3PPNbK6ZXRuqXa37XAU3bmfAe1KzRwKPRp/n4M8bYChu7F5mZo+a2R0hvtCaw4BfSvprpbXGGKvEJ/85wEgz2zTSzI0F1o6yWu2K+d9hZgvw8JDvtFpbpbLO9tmetd8X79q55iqASZIkSVIX6m3gTo0/ZwLrm9nq7WjzhqTb4/hR4PnY4Q5u4G7ejj6K2NBHcS9scV5uvzseo/lAeCqvAwbE5+8HgIlmdpa53Our0WYfYFz8T352nG8WZb8GrjKzY4ENWseatuJq4GAzG2BmW+NexEL+dQ8zmxkqVCez/M7+RSwzwsFlVzcOo/o44PL4JN4tSJoiaUtgFHB6fO7vSo4ArpX0TunaKbj3f2PcO3mxmRVpsw6V9Bn8Hv2RZc+5zEXAG8DFAOZKdd8GRkafJwI/D+Ozf6ytKWJrLyMM41YcDlzRngXJ5ZW/E3ObCRShF0va2X5PYH38x84Z7S3rRJ9trX3j8PTvjBvDZUW3JEmSJOlR6m3gLgKQtDTOB+D/Yy/Pq7XRu7h0vJTlP8MvpX1hF63HLfoot++Hx3qOKP23vqS/SpoG7ITHbp6Gb1gq2hxTqv8ZSQdF2VdxY2Et4P+a2R7VJifpOeAJ/DP+BNyoaDGzTwL/icvYNuAGX/n+vCmppdTPS5LeiuPH8A1E29e4L23d+3YR8ZcvALvUqPYc8MnS+cbA/1arHD9+DqZkOIZ07H6SboxxhW902iHO/zf+XIpvRBsVHtui/SRgC+DAiD8FDy14Nfoi+t4Mz9P7HJ5j9jdRdgvwiZhH0ecoPNTljmprjfrvRqgEkqaG138H4F7gL5IWttWuIOZ+Of4lhDbKOttnzbWX7vVC3Pj9XOu5JEmSJElPUW8DtxILgFXNrPCkjq3TPH4JjCl/hjWzkfHn5vgO+SY8HrQwGmcAJ5rZGlFvkJl9JjZ8fUrSw5LOi763aWP8Jjws4mA8ZAE8JvJtYEEYakfV6qD8ST6M41F4XHA1/gx8ysw+HB7Lg6vUWwisWdrIhrlEcXG8Kb6+J2uMdRf+aX6LOD+K5b3Prfkq8CctL7/7CrDYzHaOcT+Oe2ufDO93ebPZwcDcwpA1s3Nwj+9XJJV/ND0LbBtxtJjZ6FjvS7iH/83inYhx/w6UQzGOAK6RVPbANgNrmNnnS2u9qSiMeRdG/A+BSW21M7N1y4Y1sD9u3Ncs62yftdYe70vxzg8Avg7MJkmSJEnqRK9LEyZpiZkdB9xjZi8Ct7fVppvm8SczG4d/1l8D39T1W+AR4ADgEDN7G9/kdFw0Ow/4AfCImb0bZT/EDccm8/Ra7+KeytPamMIt+GauR8Kji6S5ZnYTbji+hHsJd67Rx7fMbF+Wfe7+Xnhyq635BTP7d9yY+Su+2el9Maby9FzXAXPN7JWIw/1BGD/v4J7w70j6Q42xXjffeHebmfXH42ePAzCz9YE7IrSi4H2f/SUtNbMDgQuij/7Av0p6wszWAm433/jXD/gLcFD0vxUeG/0U8Dszg0ihJqnZzH4M3B/PdzG+4a8l2h4OXBkxpv8AvloqWwOPW92h1TzfNbNDgZ+GETsfGFeqcmX8AFkND9u5sB3tPo6/U6vG+p5tT1ln+4wvCBXXbmZbRn8teKq235EhCkmSJEkd6dfS0tJ2rSRJkm6iubl5E+DZhoYGBg7sfXvT2qNk1tzcTGNjYw/NqOvp6/OHvr+Gvj5/6PtryPnXn2INixcvZt68eQCbNjY2zu9MX70xRCFJkqTXkDK9SZIkfY9eF6LQFZjZRCqnC5tQyrhQd8xsFu9/BjMl1Yyt7YJxp+DxuGWW6P35brtirBn4BrIyz0nap6vHSpIkSZIkgZXUwJV0Gb6Tu1fTHQZlO8ftVgO61VhpyCZ9kneWtrBq/371nkaSJEnSCVZKAzdJkr7HKXe9wOtL+9d7Gu9x5Vdbf3hIkiRJ+gpp4CY9gplNAPaS9PUKZbtQWRK4bpTna2b7ADtJOqXOczoCl3teimfGOKHIS1ulfhMuh3xxN89rFTzVWAOeU/pvwFGSnunOcZMkSZKkGrl7og5ESqukjyBpRj2N28jpOwS4ANgt0qedCfy0XnOqwFXAZ0JCejpwSZ3nkyRJknyA6RUe3MipasBA4Gk8Wf5NwEWSpkedvYCTJI0OUYErcVWw2bi87tmSbqvS/y64cfAQ8Fk8P+1BRZ5WMzuVZYpNjwDHSnqjSl97AueULg0F9pc03czGA8fg9/U14GhJCm/gOOB1XDlrXCT3PxfP3foicKSkp82TsjbhEsL9cRWzSVQhco+eDuwHDMFla6eZ2Sa49+6jUe+98+IYuBQYA6wBHIIn/d8BeAvYV9KCKmOugkvb7orniX1D0uciyf/tMY81gIdjXW9X6ONsPC/tK8B9rcra/TxK9+AM4Csx9jeA3WJtq+LPp3jW1Z7Rarhs7654juHHSv1PoOR97uD7cgYwRNIJcT4EEK4mNgo4G1eMGwD8SNLUqHcf/m6PwgUVDsNz0w7CcxQPBp6vdk9KNJjZr4GNgN8D4yN37Vg87/BqUe9kSb+KsT+DK799PMacJOkqM/tE3KON8ef7M0nnhHhGWbb398Dx7ZhbkiRJknQLvcWDe5yk7SRtjUvUnoobeeNLdQ7HjVpwadyLQq72AmBkO8bYCpgiaRiumHUGgLlk7qHAjsDWuFFZNUm9pDsKKV5ciGEWcLeZ7YQLQOwsqRE4n+WFCUbhRkQDLmN7DXBIzOd64LqodwwwQ9LwqHt5O9a2UNLIWMeF7agPbgg+KGmbGONXwOSYTzOVs1AUDAdGA0PDY7dXXF8KjI1Qgwb8Xh7RurGZ7Q3sg6uOjQK2LJV16HmUeDXuwam4B/G3sbar8R8AtPGMjgQ2xX+wfJEqksadmN/VwEEl1bex+PN9E3gU+HzMczdgkpl9uNT2U1G+p6SXYo6Pmtlz+I+sY9pxXxqAPfH3vzHGAbgbGBVjH0So5cU8pwOXShoWfyeLH45XAxdK2j762sPMvlRhzG+zvMGbJEmSJD1KbzFwDzOzZjObixsAI3Alr53MbEh4vb4ATDOzdfD/aV8PIGkWteVnC1RS8ZoJbBbHuwFTJS0MRapLWGYEVMXMdgdOBPaRtAjYGzf8HjKz2biq2UalJg+WYhJ3AOZIKqRsrwRGmNkg4AFgopmdZWa7Aq+2Y21TS+taPxSq2uINSYVK3KPA86UUas24V7waf8Y9o5eHKlbBKsDJsf7HcW/oiArtRwM3SHpD0lKWN+I79TyAG0praSl588trqfWMRgNXSXpH0j+Aa6uM06H5hQrdE7iRCTAB//EGsC5ws5nNww3Oj+BfMgquLyR/473/NjBS0sb4u/dzc0nlWtwqaVF40R9l2Xu/Gf7D7An83n08vioYMEDSe1LCkl4OZbhdgAvj3j0MrA98pjyYmX03rp3RxrySJEmSpNuou4EbXrWjgTHhLToDWD2MjOm4wTsWmB5er4KOSrAtKh0vZQXCM8xsODAF/4z/UlzuB1xReHfDA1vehl31E3sZSdOAnYBncDnfa9rRbFG0XRrnA/BNSOXn29roXVw6XkoH7o+k13CP4FRgGPBEGEdjgc/jG7K2Bn5SYdzuopj/Ut6/tmItbT2j7qIJGG9mWwMfAoqNYf+Nh2dsHV8Enmf5+1V+Z76Me6kFIOlG3Ej9aBtjV3uuPwN+ImkrYFv8fan1rFbB/86NLN2/zSS998XAzI7F34E94+9vkiRJktSFuhu4eCzha8DLoXFf/qTdhHu8JhDhCZIW4h6xgwHMbFv8U3FnuRc40MwGhTdsInBPtcpmtgEwDRgn6alS0S9wT/SGUa+/mVXTzJsJDDez4tP8eOAxSa+b2ebAAklNwA+p8qm8HSwAVo3+wA2PLsHM1gXWlHQ3boS/hn9OHwy8FOv4UI0xfw0cYGZrxYa7w0tlHXoeHaTWM/o1cGhs6Fqjxtw7M79bgJ2Bk/CY6uLH2WBgfsTEfonaXvNngW3NbL2Y+2hgIR4v3BkGR5/gf+cKjVwBS8xs/6KimQ2R9DpumJ9Wur5R/LDBzI4Evgl8SdLfOzmnJEmSJOkSeoOBexfurXwKuB//jAqApAeBdYB14rjgMOD4CGk4GZiLG1kdRtKd+Ofo30c/4Bt/qjER/7Q82cxmx3+jJT2Ax3rOMLM5wDxg3ypjvojHcV5vZo/jG9DGRfEBwFwzewzf0HNcJ9e1JNreY2YP4967rmIj4N5Y5+PAnbjRfjUwyMz+iBuTFVNYRfjAbcCcaPdUqayjz6PdtPGMLgGeA/6AG7uPVOmjw/MrfY04FL9HBafhcbez8edeNdRGUjPwY+D+mPu/AV8vGcsd5XjgVjN7FP9x8nKMswS/J0eZ2dwYqwivOAQYGtfn4qENgyO05r+BtfH3bbaZPdTJeSVJkiTJCtOvpaWz/3+sH2a2NvBmeL6G4p95TdIr9Z1ZkiQdpbm5eRPg2YaGBgYOHNhW9R6jI0pmzc3NNDZW+2DT++nr84e+v4a+Pn/o+2vI+defYg2LFy9m3rx5AJs2NjbO70xfvSJNWCfYETi/tMHmG2ncJknSlaRMb5IkSd+lTxq4kn4J/LL1dTObgefoLPOcpH06OoaZfR/4aoWiL0v6W0f7WxHqNRczm0jldGETShkXeoTe9DwqUcdnNIJlWRnKXCzpsu4aN0mSJEl6M33SwK1GZwzZGn2diatF1Z16zSUMpF5hJPWm51GJOj6j2VROxZZ0kI6EJCRJkiS9m5XKwE2SpO9ywd1/5a2l9fsn6Qf7rV+3sZMkSZKuJQ3cZIUxs38FfoDnc51nZp8Gfgp8As+v+ghwjKS3OtDnOOC7uLLY8ZIubqP+6riow1uhpIaZrYnLEQ/Hc+DOAyZGyivM7F/wFHTg6bvOiutr4zl8t8EFLS5TK7nkSJU2D/iNlkn49seV5MbgOWPPK8IEzOxqPGdwwTDgK5JmtNHu4/i93DTm8iNJ10bZD3A1sxeiz99K+lZp7VfiimNLcBW926LsdOBAPLNGP+BcSTdEWRMuXFGkH7tJ0o+ibBQu4Tsw5nKRpCmlsv/E5bMX4xLN72VESZIkSZKepDekCUv6GCXZ2SIP8Sjgf0pV3gZOlLQlbsitiadz6wizcQnZ69tZ/0d4yrEy3wRWw/MkF9LBR8e8dwb2j+sNwP5xDeB7sYZhuIF4aBhwZX4C3NHq2iF4LtstgM8CPzCzTQAkHaZlEs/jgVdw9bKa7YD/AGbJJZR3Bs4xs7JC3tUl4YVvla6fjEs4b44ruF0Whjt4fO6wkOndE7i0lUTweaU+f1S6PgU4K9bwRTzF2cdis+c04NSY5wnAte1QWUuSJEmSbiE9uHXEzFrwvKz7AUOAUyRNC+NmlqSPRr33zotj3DM5BlgDN5COwiWA38IV1hZUGXMc8DVJ+8X5ADz/6+fwPKY/wb1wqwOXSLog6jXhnkADBuHSwgOBybjoxn3FGJLmA/Pj+N3Iw7ucpGtbSJoX477bVt1Qw9sCNwaHl4pacON61ThfC1cLA/dgXl14lcPDeiAulTycZYIMb5rZ/fg9nhl1DwH+ij+HvUrjHQhcKuld4EUzuxU3os9vNeV/Aq6TtLgd7YbjnlEkvVjKmfvvbdyWA3FDGkl/MrNZwB64R7acM3rtuE/t+bHbgiuxgb8DC4E3cTW1wZFnGEkPhpjGtrhXPUmSJEl6lPTg1p+FkkbiIgAXtlU5GAI8GB64y4FfAZPDe9ZM5cwHBbcAO5lZIfG6B/BHSc/iRulukrbFFdS+aWZlw3QELqlcbGo6E7g2DNqKhCrYEcCMdq6tQ5jZWsAFhGe2FT8FXseN0b8Cr0kqPMIbs7zX+TlcwAL8Hn7dzFaN+7Q78MkYb33gREqKXiVq9VnMdzVcJe2KdrZrBg4ys35mtimeIu+TpboHmdnjZvZLM/tse+diZkeFIMdjwDclvVyqe2KIOdza6vkfjnuQn4t2x0h6I4RLXjKzfaPvvXEDuDzPJEmSJOkx0sCtP1Pjz5nA+hFL2hZvSLo9jh8Fni+l7WqmhuRrqGrdyjIp2gksSzO1JnB5qFT9Flif5T2iN0t6EyCMqe1wj29Fwjs8Ffi1pG4xcHEv52RJf6lQtlv8+Yn4bzUza0+oxHl4DOosfP734d5rcM/5dyW90cn5fgVPXdfeNGsnAR/DQzYuxH/MFHOZAmwaP2zOB6ab2ZD2dCppSoSQjAJOL7U7Hdhc0tb4j6G7IkYY4BT8K8PGeOjGxWZWpOXbDzg2lNH2wOW0i3kmSZIkSY+SBm79WQQgqZDSHYAbBuVn09roXVw6Xlr0UTpvK/SkCRgfRs0XgJvj+jnAAmAbScOBh1uNXTbqvoCHHTxrZvOBDYG7zezL8N6Gq+vwWNPv1JqMmW1dkj3+zzbm3prPA9+POUwFtg75Y/CwjVskLZK0CJeWHR1lz7G8h3Fj4H/BfwRI+pak4ZJ2w+/pk1Hvs/iPgPnAJGAPM7ujrT5LHMHy3tu25vKipHExl8Iz+mSULZD0ThzfE20aOjAXJM3FN6ntEud/iVAJJF2NhzBsGJ7s/STdGGXCpYp3iPNHJRXe/+OBDUr3LEmSJEl6lIzB7Z0sAFY1s80lPc0yb2uXEDGS6wDnAreGVxdgMPC4pCVm1gDsRJVNXpLOwz2dAITBt1dkUVgFN6KXAv8Usay15jOXTuZyDe9lMYddgElFFgXgWWB3M7sJzxYwBs98AHATcKGZTY7zw4Bjo591gHckvWVmw3DvZGOM95HSeBNizV8v9fkNM7sFDyP5Cn4Pi/obxvnBrZZRtV38CHktnsmu+Ia5ImvDBoXnOgQfNgFU6vNIYJaZbQGMLMY1s6GSnozjTfFsEU9W6HN3/Bn+BY+/XWxmO0t6ILI7jCi1+3gp7vufgfvj3U2SJEmSHicN3F5IGDPHAfeY2YvA7W216QRXAWdRMsCAs4FrzOyfgKfwDVedYQ9gHG5MNpsZlFJYtQczOxj/7P5hYF8zOw1XBXvSzM4EXihSVNXgh8AlLDNq5+DZFpB0XxiUT0TZ1ZLuj+NPATea2RLcO36IpBdom2twj+af4vzMiG0uGA/8Qu+Xla7VbnvcEF+Kh03sXfpBco6ZNeJG6NvAoSUj83ygycyejvJvKtKj4VkatgLeibLvSPpDlF1lZh8D3sU3ke0jaQmAmR0IXBDe+f7Av0oq7t+RZjY2rs/CPdVJkiRJUhf6tbTUdK4lSZJ0K83NzZsAzzY0NDBw4MC6zWNFlMyam5tpbGzs4hn1HH19/tD319DX5w99fw05//pTrGHx4sXMmzcPYNPGxsb5nekrY3CTJEkgZXqTJElWIjJEYSXFzCZSOV3YhA7s4O9SzGxPfCNba74nqbVoQpIkSZIkSadIA3clRS71elm951EmjNg0ZJNex5KlLQxID26SJMlKQxq4SZL0Cq65+yUWL63PP0nf2u9jdRk3SZIk6R7SwE2SXk6FdGTlsl1YPjVaXTCzI4AT8KwMS4ATJP2mnnNKkiRJPrjkJrOkT1FS1Up6AWY2IHL1XoDLPI/AJZx/Wt+ZJUmSJB9k0oPbxzGz6wADBgJP4/lHbwIukjQ96uwFnCRptJkNBa4E1sLlXzcHzpZ0W5X+d8GNl4dwFa8W4KAib6qZnQocGtUfAY6tJmNbYZPZUGB/SdPNbDxwDP5OvgYcLUnhvRwHvA5sAYwLkYFz8ZyrLwJHSnraPOFuEy453B9okjSpxr1rwaVp98MFFk6RNM3MNgFmSfpo1HvvvDjGJXvHAGsAh+CqaTsAbwH7lvLRth5zFeBiYFdcke4NSZ8LWePbYx5r4CpyR0p6u0IfZwMH4Spx97Uq68jzOAMYIumEOB+CC0V8EpfwPRtXshsA/EjS1Kh3H/7ujAL+jotk9MNV1v6KC4Y8X2nMJEmSJOkJ0oPb9zlO0naStsZFC04lpHhLdQ7HjVpwUYGLJDXghuvIdoyxFTAlVMNuBM4AMLM9cGNqR1xhqz/wL9U6kXSHpBHh5ZuMG4p3m9lOwAHAzpIacZGCspztKODkmPMLsYZDYj7X45LA4AbyjJC1bQAub8faFkoaGeu4sB31wY3QByVtE2P8Cpgc82mmcvaKguG4XPDQkEPeK64vBcZGqEEDfi/fJ5ZgZnsD++AqYqOALUtlHXoewNXAQWFcgyvmzZD0JvAo8PlY427AJDP7cKntp6J8T0kv4appj5rZc/iPmGNqjJskSZIk3UoauH2fw8ys2czm4gbKCOAWYCczGxJeuS8A00KCtoGQ35U0C3i8HWNI0mNxPBPYLI53A6ZKWhhyvJfEtZqEBOyJuErWImBv3PB7yMxm4xLAG5WaPCjpmTjeAZhTSM3ihvsIMxuEK69NNLOzQtb21XasbWppXeub2ertaPOGpEJd7lHg+VLqtWbcK16NPwOrApeb2aGl66sAJ8f6H8c9vJXki0cDN0h6Q9JSljfiO/Q8JD2H/yjaMy5NwH8cAawL3Gxm84C7gY/gXwoKri8pnK2DG/UjJW2MP9ufm1mmJUiSJEnqQhq4fZjwfB4NjAkP7hnA6iHlOh03eMcC08MrV9BR+bpFpeOlrEBoi5kNB6bgn/Ffisv9gCsK7254YDcuNav4ib01kqbh0sPPAKfhnt62WBRtl8b5AHyTVPnvRmujd3HpeCkduD+SXsM94lOBYcATEXIxFvg8sFM8y59UGLc7aALGm9nWwIeAYmPYfwFnKSsAACAASURBVOPhD1uHx/35VvMpP5MvA69KEoCkG/EfQR/t1pknSZIkSRXSwO3bDMbjVV82s4Es/0m7CffITSDCEyQtxD12BwOY2bb4p+zOci9woJkNCm/dROCeapXNbANgGjBO0lOlol/gnugNo15/M6umNzgTGG5mxaf58cBjkl43s82BBZKagB8C23dyXQuAVaM/cOOzSzCzdYE1Jd2NG+Gv4Z/7BwMvxTo+VGPMXwMHmNlaseHu8FJZh55HcAuwM3ASHrNc/PgZDMyX1GJmX6K2V/pZYFszWy/WOBpYCLxUo02SJEmSdBtp4PZt7sK9lU8B9+OfywGQ9CCwDrBOHBccBhwfIQ0nA3NxI6vDSLoTuBb4ffQDvjGpGhPxT9+TzWx2/Dda0gP4Zq8ZZjYHmAfsW2XMF/E40+vN7HF8A9q4KD4AmGtmjwEXAcd1cl1Lou09ZvYw7pXtKjYC7o11Pg7ciRvtVwODzOyPuMFfMcVWbAa8DZgT7Z4qlXX0eVDy9h8acyg4DY+7nY3f16qhLJKagR8D98e6/g34eslYTpIkSZIepV9LS/4/6IOEma0NvBmeuaH4Z2iT9Ep9Z5Z8UGlubt4EeLahoYGBAwfWZQ4rqmTW3NxMY2O1jw69n74+f+j7a+jr84e+v4acf/0p1rB48WLmzZsHsGljY+P8zvSVacI+eOwInF/aAPSNNG6TDzop05skSbJykQbuBwxJvwR+2fq6mc0ANm51+TlJ+3R0DDP7PvDVCkVflvS3jva3ItRrLmY2kcrpwiaUMi70CL3peSRJkiRJT5AGbgJAZwzZGn2diatZ1Z16zUXSZcBlPT1uJXrT8+itLF3aQv/04iZJkqw0pIGbJEmvYPpdf+edJfX5J+mQr61bl3GTJEmS7iGzKCRJUhUzuyzyLXe0XZOZ1VJ0S5IkSZJuIz24SbKSYWYDCpWxFUXSxK7oJ0mSJEl6kjRwk6QLMLMWPJfvfsAQ4JRQVsPMxgDnAv2BF4EjJT1dpZ/1WH4T4MbAVZJOMLMdcBnjdaLs+5JuN7NNgFm4uMeuwCVmdi/wUzzv8BLge5LuMrM1gatwNbV3cBnmA2qs6z5gkqTbzKwJV237NJ7P9/fA+Eg5twGeR/cTwHzg3XbctiRJkiTpFjJEIUm6joWSRuKiCRfCewbrNcAhkoYB1wPXVetA0t8KyWJcpexV4GIzG4xLHI+V1AjsBfw0roMb1Y9I2lbSlBjj+hhzHHBtqKjtjot/DJU0HDiyg2tsAPbEDeRGYLe4fiHwgKShePaIL3Sw3yRJkiTpMtLATZKuY2r8ORNY38xWB3YA5kh6MsquBEaY2aBaHZnZRsDNuGH8DJ6/eFPgzlAXuxNoYZmE7iLgxmg7CBjBMonmJ4HZwChcAe0zZjbZzPYHFndwjbdKWiTpbVw5b7O4PprIGiHpz8CvOthvkiRJknQZGaKQJF3HIgBJS80MOvn3y8zWweV4T5X0+7jcD3hc0s4V6m9CqNO11bekP5vZVsAXgT2Ac8xsa0mL2jm9cr2l5L8hSZIkSS8kPbhJ0r3MBIab2ZZxPh54TNLrlSqb2QDcc3uNpJtLRb8DtjCz0aW6I0uKdO8Rfc+OsTCzzwDDgZlmtiGwVNKtwAl4jO5HVnCNAL/GQyows01xAzpJkiRJ6kJ6X5KkG5H0opkdClwfxuuLeExsNT6HG4frmVlR7zpJ55vZPrjM8gXAasCfgb2r9HMIHqN7Ar7J7NCYyx7AeeFh7g+cK+mFFVwmwHHA1WY2FngWuK8L+kySJEmSTpEGbpJ0AZL6VTuXdBdwVzv7uR83PCuVPQLsUqFoPvDRVnWfpoIXVdKdePxuu5C0S+l4QquyCaXjv1QaryPsO+YjDBw4cEW66DSpZJYkSbJykSEKSZJ84EnjNkmSZOUiPbhJUgfMbE/gnApF35N0xwd1LkmSJEnSFaSBmyR1IAzHXmE89qa59DQZmpAkSbJykgZukiS9gnvveIWlS3r2n6R99v9o25WSJEmSPkcauEnShzGzIbhS2mbA28CfcCngF83s07hc7yfwTAqPAMdIequTY80H9pI0z8wmAL+T9FSUjQCuwOP6VwV+CxwrqaNCEkmSJEmywuQmsyTpo0TasRbgx5JM0tbAM8B5UeVt4ERJWwLDgDWBk7to+AnAp0vnAkaFxPDWuHRwR2WAkyRJkqRLSA9u8oHHzFqA04H9cMPsFEnTQiFslqSPRr33zotj4FJgDLAGnnv2KFye9y1gX0kLqow5DviapP3ifADwHJ4Hd23gJ8BawOrAJZIuiHpNuDfWgEFhUN5X6nomcDSApPl4CjEkvWtmDwOfaeNezCe8tJXO49rhwHbAhWZ2NnCypHtL3awa9+PdWmMlSZIkSXeRHtwkcRZKGgkcClzYzjZDgAclbQNcDvwKmCxpGNAMfLtG21uAncysCALdA/ijpGdxo3Q3SdsC2wPfDDWyghHAmDBu38PMVsGN2xmtBzOzNYAjKpV1FElX4sb9dySNKIxbM1vfzGYDLwGvA5es6FhJkiRJ0hnSwE0SZ2r8ORNY38xWb0ebNyTdHsePAs9Lmh3nzcDm1RpK+gdwKzA2Lk0AmuJ4TeByM5uLx7Kuj0vtFtws6c0K3V4EvAFcXL4Y3uGpwK8lrbCBWw1JL4TR/XFcae2r3TVWkiRJktQiDdwkcRYBSFoa5wPwUIDy35HWRm95A9XSoo/SeVshQE3A+Ngo9gXg5rh+DrAA2EbScODhVmO/0bojM5sEbAEcKOnd0vX+wHXAK8B32pgPtL3mNgnj+wY8ZCNJkiRJepw0cJOkOguAVc2s8MSOrVW5o0h6EFgHOBe4Nby6AIOB/5W0xMwagJ1q9WNm5wCNwFfKWQsiZKEJN7b/SVJLO6b1NDAy2n8R+FiVeguBD5XG+pSZDYzj1YB9gbntGC9JkiRJupw0cJOkCpKWAMcB98QGraVtNOkMVwHfYFl4AsDZwDfM7HHgB8AD1Rqb2VbAP+NhDL8zs9lm9vMo3gMYh2c1aI6yyW3M51+AkyKW9v/gG98qcQnw/ehzN2BHYJaZzcHDNf4OnNXGWEmSJEnSLfRraWmPUydJkqR7aG5u3gR4tqGhgYEDB/bo2F2lZNbc3ExjY2MXzKg+9PX5Q99fQ1+fP/T9NeT860+xhsWLFzNv3jyATRsbG+d3pq/04CZJ8oElZXqTJElWTjIPbpJ0I2Y2kcrpwiaUMi70KGa2J76RrTXfk3RHT88nSZIkSbqaNHCTpBuRdBlwWb3nUSaM2A+8IdtV4QlJkiRJ7yMN3CRJegW/nf533l3Sc/8k7TZ23R4bK0mSJOlZ0sBNeh1mdgdwrKRn6j2XjmBmBjwG/ETSyXHtdOBAPANDP+BcSTf0wFzOBJ6QdIOZ7QKsJumXK9DffcAkSbeZ2VeAFyQ9HGUDgem4fC+FtHGSJEmS1IvcZLaSEYn9+zSS9uwrxm1xv+PPn+LqZGUuljQs5Hz3BC41sw9397wkfb9kSO8CfLkLu/8KLiFcsBSYBOzWhWMkSZIkSadZ6T24ZnYdYMBAPIn9EcBNwEWSpkedvYCTJI02s6HAlcBawGxcbvVsSbdV6X8X4ALgIeCzQAtwkKQ/RPmpwKFR/RHcM/k+Jaqo23rzz1Bgf0nTzWw8cAz+zF4DjpYkM5uA5zp9HVeyGmdmH8fFA/oDLwJHSno6PIxNuBRsf6BJ0qQa964FOB3YDxgCnCJpmpltAswqPHXl8+IYuBQYA6yBK1odBewAvAXsK2lBjXHnA3tJmheew2bcoNoE+C/gL8CxeO7XUyTdVJrvmbjIwBr4pqlpVcZYE8/xuqWkl+LaJOB1ST+s9N5IeiWe94Uxp22AM4DbgNPiz7XjPwAkvVYadm38/aj5w7KNZz0WeBUYVroPk/D39BFgnKQWM2vCn8P9+L1fJfLVTpV0XpVx7yO8tJXO49ruwD7AbrGB7j8kXQ3cG88+SZIkSerOB8GDe5yk7SRtDTwBnEpIpJbqHI4btQDX4MZvA264jmzHGFsBUyQNA27EjR7MbA/cuN0RT7bfH0+kXxFJd0gaIWkEMBk3UO42s52AA4CdJTUC5wNXlJqOAk6OOb8Qazgk5nM9LtUKbjTNkDQ86l7ejrUtlDQy1nFhO+qDG8MPhtfycuBXwOSYTzOVswrUYkNcynYH3IBtkLQjfk/+s1XdpXH/9gEuMbP1KnUYqmG3EupkZjYgjq+KKpXem4KtgEviWd1mZsOB3SvMhej7KDP7Ix6+8E1JL1dbaDue9UjgRElb4j8Wro95D8XfsS+2WudcYApwdcy3onHbXiTdDcwAzov+rl6R/pIkSZKkO/ggGLiHmVmzmc3FDYERwC3ATmY2xMyG4MbTNDNbB2jAjQYkzQIeb8cYkvRYHM8ENovjwmO2MGRSL6Edn3HDS3YisI+kRcDewHDgoVCYOg/YqNTkwdIn/R2AOZKejPMrgRFmNghXxJpoZmeZ2a64J7AtppbWtb6Zrd6ONm9Iuj2OHwWeL6XEasa9jR3hJknvSnoBeBkolLqagQ1azely8AcSY4+q0W8TMCGO9wD+KGl+nFd6bwr+JOn3AGa2Kv5cj5JUUelM0pQwSEcBp8c7V422nvVvJT0fx4/hz/7VUF2bQ8fvbZIkSZKsdKzUBm54w44GxoQn7gxg9fDeTccNl7HAdElvlpp2VN5tUel4KSsQ+hHewCn4Z/yX4nI/4IrCuxse2I1LzSqGPLQmPtfvBDyDf1K/ph3NFkXbwngbACxh+XentdG7uHS8lBW/P63bV5pTh5H0IDDIzLbGDd0rofp7U2pavt+fwH/Q3BGhFcfjMruXVBhvLu5h36XGtNp61hXvRel8RcKO2nquSZIkSdInWKkNXGAwHsP4cuz0PqJU1oQbNRMIw0bSQvxz9MEAZrYt/tm3s9wLHGhmg8ysHzARuKdaZTPbAJiGx1E+VSr6Be5R3DDq9Tezanp8M4HhZrZlnI8HHpP0upltDiyQ1AT8kOU3CnWEBcCq0R/EZ/5ewuEAZrYFHiM7s436VwEnATvj9x5qvzfLIek5SR+VtImkTfCwlkslfTPmMbSoa2abxpyerNiZ05Fn3V4WAh9qR72niZCcmPeIKvXa21+SJEmS1IWV3cC9C/dWPoVvtnm0KAjv3TrAOnFccBhwfHyaPhmYixs7HUbSncC1wO+jH4CzazSZCKwLTDaz2fHfaEkP4Ju9ZpjZHGAevpGq0pgv4vGy15vZ4/gGtHFRfAAw18weAy4CjuvkupZE23vM7GHcc9hbGBDruw3fXPe3Nupfjd+v6eHZhxrvTSf4gZk9EeEGNwPfKTYgVqIjz7oD/BwYGe/TaTXq/RjYM979U/EQiEpcA4yN/g4DMLNH8Pf8w2b2vJn1KnGLJEmS5INFv5aWjn6NX7kxs7WBN2Mn+lDgPsAkvVLfmSVtEVkUBlXLUpH0TpqbmzcBnm1oaGDgwIE9Nm5XKpk1NzfT2Liijvb60dfnD31/DX19/tD315Dzrz/FGhYvXsy8efMANm1sbJzfmb5W+jRhnWBH4PwIKQD4Rhq3SbLykTK9SZIkKy9p4LZCrvb0PsUnM5sBbNzq8nOS9unoGGb2feCrFYq+3I5P6l1KveYSOVQrpQubUMq40CEkvc9iiTRhlRS8bpF0ZmfG6Qq68n3q4Lgj8Pjz1lwsKcMKkiRJkpWCNHDbSVcaHmFY1c24KlOvuYQx1e0GVRjp1TZL1Y3uNmRrjDubXng/epp3l7awSnpwkyRJVlrSwE2SpFfQfMvf4Z2e+Sdpx8PW7ZFxkiRJkvqQBm6S9HJConcvSV+vULYLLqe7XU/Pq9U8WvBMIe/GpUMj72+SJEmS9Dhp4CZ9CjPrX00xLOl5zGxApI0D2DEzWCRJkiS9gTRw+zhmdh1gwEA8Uf8RwE3ARZKmR529gJMkjY7UZ1cCawGzcWnXsyXdVqX/XXDxgoeAz+IqbwcVuVzN7FQ8jyzAI8Cx1YwcM9sTOKd0aSiwv6TpZjYeOAZ/J18Djpak8F6OA14HtgDGmdnHgXOB/sCLeL7bp83M8A1Ua0ZZk6RJNe5dC55zdj9gCHCKpGlmtgkwS9JHo95758UxcCkwBlgDOAQ4CpdJfgtXoVtQZcxVgIuBXXHFtzckfc7MBgC3xzzWAB6Odb1doY+zgYOAV/A0duWyjjyPM4Ahkk6I8yGAgE/issJn42pmA4AfSZoa9e7D351RwN+BPSv1nyRJkiT1YmUXevggcJyk7UJS9gk8QX8TrmBWcDih1oYn6b9IUgNuuI5sxxhbAVMkDQNuxKVrMbM9cGNqR1zxrT/wL9U6kXRHIUELTMYNxbtDGvcAYGdJjcD5wBWlpqOAk2POL8QaDon5XA9cF/WOAWaEvG0DcHk71rZQ0shYx4XtqA9uhD4oaZsY41fA5JhPM5WzQxQMB0YDQyUNB/aK60uBsRFq0IDfy/cpqJnZ3sA++EaxUcCWpbIOPQ9c5OKgMK7BFelmhGz1o8DnY427AZPM7MOltp+K8rJxe1+IP5wbCnBJkiRJUhfSwO37HGZmzaE+NRY3fG4BdjKzIeGV+wIwzczWwY2n6wEkzQIeb8cYklSoWs0ENovj3YCpkhZKagEuiWs1MbPdgROBfSQtAvbGDb+HQvHrPGCjUpMHJT0TxzsAcyQVcrdXAiPMbBDwADDRzM4ys12BV9uxtqmlda1vZqu3o80bkm6P40eB50upzZpxr3g1/gysClxuZoeWrq8CnBzrfxz38FbKdjAauEHSGxGqUTbiO/Q8JD2H/ygqjNQJLEshti5ws5nNA+4GPoJ/KSi4vhSaALBxGOc74575WoZ1kiRJknQraeD2YcLzeTQwJjy4ZwCrh+TsdNzgHYvL0L5ZatpR+bpFpeOlrEBoi5kNB6bgn/Ffisv9gCsK7254YMs5YtsV1ylpGrATLrN7Gu7pbYtF0baI6x0ALGH5vxutjd7FpeOldOD+SHoN94hPBYYBT0TIxVjg88BO8Sx/UmHc7qAJGG9mWwMfAn4T1/8bD3/YOjzuz7eaz3LPRNL/xp8L8fRvn+vWWSdJkiRJDdLA7dsMxuNVX45PwuVP2k24R24CEZ4QxscTwMEAZrYt/im7s9wLHGhmg0L5bSJwT7XKZrYBMA0YJ+mpUtEvcE/0hlGvv5lV0xucCQw3s+LT/HjgMUmvm9nmwAJJTcAPge07ua4FwKrRH7jx2SWY2brAmpLuxo3w1/DP/YOBl2IdH6ox5q+BA8xsLTPrj4efFHToeQS34F7Xk/CY5eLHz2BgfkhWf4kaXmkz+7CZrRHHA4Cv4zG6SZIkSVIX0sDt29yFeyufAu7HP5cDIOlBYB1gnTguOAw4PkIaTsZTO73WmcEl3QlcC/w++gHfmFSNifin78kRqznbzEZLegDf7DXDzOYA84B9q4z5Ih5ner2ZPY5vQBsXxQcAc83sMeAi4LhOrmtJtL3HzB7GvbJdxUbAvbHOx4E7caP9amCQmf0RN/h/U6lxbAa8DZgT7Z4qlXX0eVDy9h8acyg4DY+7nY3f11qhLFvi4SXFmt4hQxSSJEmSOtKvpaWjX6uTvoyZrQ28GZ65ofhnaJP0Sn1nlnxQaW5u3gR4dvFT6/dZoYfm5mYaG6t9dOj99PX5Q99fQ1+fP/T9NeT860+xhsWLFzNv3jyATRsbG+d3pq9ME/bBY0fg/PiEDfCNNG6T3kDjVz/CwIE9k3whpXqTJElWbtLA/YAh6ZfAL1tfN7MZwMatLj8naZ+OjmFm3we+WqHoy5L+1tH+VoR6zcXMJlI5XdiEUsaFHqE3PY/eQhq3SZIkKzdp4CYAdMaQrdHXmcCZXdXfilCvuUi6DM8mUHd60/PoLby7pIVVBqSRmyRJsrKSBm6SJL2CJ254mX5v98w/SdtMXK9HxkmSJEnqQxq4SdILMLMfAGtLOrlC2QRgL0lf7+l5lebwCTy7wwBcIe2PwDclvWJmXyfU7YINgQckVQqLSJIkSZJuJw3cJAnMrH9J8CEJIrftS7iU8j/i2n/iqcBOlHQzcHOp/mOEWl6SJEmS1IM0cJOqmNl1uDzrQOBpXEjiJuAiSdOjzl7ASZJGR9qxK4G18ET/mwNnR+7WSv3vAlwAPAR8FldYO0jSH6L8VDw/K8AjwLGSKqqamdmewDmlS0OB/SVNN7PxwDH4+/4acLQkhWd0HPA6sAUwLlTFzsW9lC8CR0p62swMF89YM8qaJE2qMpc1gatwxbJ3cKnjA6Lvn+H5iVcHbpf03QrtV8Pz+O6KG5aPlcr6A/8GjIlLdwGnVjPMzewyYK6k/4rzBmAGLrd8MJ7vd7WofrKkX0W9+bja2q7R/p9iLcUc1qZC/uQQD9kwxkiSJEmSupBCD0ktjpO0XUjHPgGcSki7luocTiil4dK4F0lqwA3Xke0YYytgiqRhwI3Ep24z2wM3bnfE1db6U0M8QNIdhdQvMBmYBdwdcsYH4N7HRuB84IpS01G4YdcAvBBrOCTmcz1wXdQ7BpgRMsINwOU11rQ7LrAxVNJw4Mi4/iqwd8xjBLCdmY2p0P5IYFPcSP8iyyuyfTPabhv/bRPXqtHE+59XoVh2NzBK0jbAQbhRXmYdSduHcQtACD+8iP8gqLRx7QjgOklv15hTkiRJknQraeAmtTjMzJpD9WwsbljdAuxkZkPMbAjwBWCama0DNBCfpiXNorb6VYEkFR7KmbhnEWA3YKqkhWGMXRLXamJmuwMnAvtIWgTsDQzHlbZmA+fhamIFD0p6Jo53AOZIejLOrwRGmNkg4AFgopmdZWa74sZqNeYAnzGzyWa2P7A4rvfHcxDPAZrx+zWiQvvRwFWS3omQgGtLZbvhBurbYUReWeu+hIrdIDPbOkINDmaZIbsZ/iPgCeAG4OPhZS64mlbED4iPAX8AjiqXhVz0WJb/AZEkSZIkPU4auElFwvN5NDAmPLhnAKuXpF3Hxn/TJb1ZatpRabxFpeOlrEDYjJkNB6YA+0p6KS73A64ovLvhgS3n+60Y8tAaSdOAnXBp5NNwT2+1un/GPdP34MbnHDNbHTe8PwzsEB7iW/FQhe7mKmACsAfwB0n/E9d/BvxE0la4N3hJq/lUvDeS3ok+D21VtB/wZ0nt+WGTJEmSJN1GGrhJNQbjMZYvh2fuiFJZE24wTSDCEyQtxMMYDob3YjG3XoHx7wUONLNBobo2ETcYK2JmG8D/Z+/dw60qq77/D4IiKqShZp7CMkfpRtCtpSYm5vlSOzx5RkDDPJSpSelT6q/M0l553nw0ijwiJpqmPyHPp0zpEdMlp632JQviMaXUzFOyFdzvH2MsmWzXWvt8wvG5rn0x532a455zbfZY4x7z/nIzMEbSwkLVb/BI9ObRrr+ZVdMynA2MMLNPxPk4YI6k18xsa2CppKnA91k1baC5LZsDKyTdCpwObAR8EL+nz0taFvZ+vsoQDwDHmNkAMxuEf5Eocx8wzszWNLM1w8aq9yWYhj+XCaxMJyHsWRTHx+G51tXmtEXIPGNmawD/ASxo1uw4MnqbJEmS9ALSwU2qcRcerVwI/A54olwRy95D8BzNWYU+Y4HTIqVhIu4AvedFpNYg6U58af4RVjpS59foMgF3JCeb2dz4GS3pIeC7wMxIDWigimMp6QU8KjndzObjL6CNierDgAWxQ8Cl+MtZ1RgOPBLX+wNwgaTngEuAz5hZA57De3+V/pcBS/A0gAfwF+yKdfPxF8/mxPHlNWxB0hLgKWBPPMWkzGnArWb2BPBR4KUawxjw+7gv84EPA994t9JsC+Az5O4JSZIkSS+gX1NTW1eUk6QyEeF7Q1JT7KjwIGCSXu5Zy5LeTKlUGgYs6vfkh/us0EOpVKK+vtrCQO+nr9sPfX8Ofd1+6PtzSPt7nvIcGhsbaWhoANiqvr5+cXvGym3Cks5kN/wlqrIG6vHp3CatZbvDhzJwYNUsiU4lpXqTJElWb9LBTToNSfcA9zQvN7OZwJbNipdIOqSt1zCzc4FKCln7SvpHW8frCL3Mlin4lmdFlkvaqTvt6Cukc5skSbJ6kw5u0uW0x5GtMdZ5VN5/tdvpZbac2HKrpExGcJMkSVZv0sFNkqRX8JdrX2SNxu75L8m+9qFuuU6SJEnSM+QuCkmSJEmSJMlqRUZwk07DzMYDB0n6coW6PYFJvSkntGivmR0CjJL0rR626TpcyezDwGBJr0f5NsAvonw5vnXYyZLerDHWg/g9v60b7L4Vlxd+BxeIOEXS3K6+bpIkSZJUIiO4XYSZ9e9pG5LWI2lmTzq3IaMLvj9uJfnet4BvSvoEsD2wDr7XcG9hXKjE7QBMIgUfkiRJkh6k2yK4EZkyXC3pGVz16CbgUkkzos1BwBmSRsc+qlcD6wJzga2B86tFoyJCeDHwKLArLhl7hKSno/5MVkqLPoZHmCpKkZrZgcCPCkXbAodKmmFm44CT8Xv3CnCSJEU0cAzwGvBxYIyZbQJcAPQHXgBOkPSMmRmuBrZO1E2VNKnGvWvCxQq+CAwFviXpZjMbBjwuacNo9+55+RgXAdgfGAQcDZwIfBp4E5e0XVrlmmsAPwX2AhqB1yV9Jhyx28OOQbiQwQmS3qowxvnAEcDL+J64xbpWP4/CPTgb+EJc+3hcBnd/YE38+ZSfdbVntBYu0rAX8CIulFAefzyF6HMbPy9nA0MlnR7nQwEBH8F3Njgfl8AdAPxQ0g3R7kH8s70L8E/gQEkPRN0q15C0GFgcx++Y2R+AT1a7XwU+a2ZnAZsCN0o6K8Y/A382A3C55JPKEVcz2xW4CBgcY3xL0j3xub0Y2BBYC7hYUlnJrijo8QE8kpskSZIkPUJ3RnBPlbSTpOG4pOuZuJM3rtDmWFZKiV6LO791+B/VnVtxje2AKZK2B27EHSLM7ADcWdkNV5nqD5xTbRBJd0gaKWkkMBl3FO82s1G4otUekupxJ6AYqdoFUZnmPAAAIABJREFUmBg2PxdzODrsmQ5cF+1OBmZGxKsOj9q1xKuSdo55XNKK9uCO4KyIqpWVsyaHPSXg6zX6jsCXyreVNAI4KMpXAEdFqkEdfi+Pa97ZzA4GDsGjkbsAnyjUtel5FPhX3IMzgRnA72Nu0/AvALTwjE7Al9G3BT5HFbnddtg3DTiiEIU9Cn++b+AKcLuHnXsDk8xsg0Lfj0b9ga2Yf9m+Qfg9n9mK5lsCewA7ABPM7ONlmyXtHHadA0yJsT8I/P/At+O57wg8FnObDpwez2B34KyCrDFmdoWZLQF+yKq/10mSJEnSrXRnDu5YMzsaj/ysi0vAngf8JCJeAJ+NdkNw52k6gKTHQyK0JSSpHJWbDRwcx3sDN0h6FcDMLgP+u6XBzGw/4Jt4buaycNpGAI9GhK0fUHRWZkn6cxx/Gpgn6ak4vxr4mZkNBh4C/o+ZrQP8Nn5a4obCvDY1s7Vb0ed1SbfH8RPAs4W8yBKwT42+f8Ejo1ea2QNAOXK+BjAxnMD++Pz/XaH/aOBXhRzSK4kvHLTzeQC/KsylqRDNL7FyP9paz2g0cI2kt4G3zeyXuKPWnDbZJ2mJmT0JHIg7neOB06N6I+CqcCyXAx/EVzJmR/10SctbMXfClgH4Z+EBSa1xcG+S9A7wipk9DXwM+BNQb2bfCXveAbaJ9rsCT0n6n5jbCuDlWFH5JHBDIbo8MMr+GG0nhI3H4F8sWu20J0mSJEln0i0ObkTVTgJ2k/SCmR0FfFXSv81sBh7xApgh6Y1wcMHTDNrCssLxCjowPzMbgUe19pP0YhT3A66SdG6VblWX2ItEesEjwL7AWXg0bkwL3ZZF3xXhYAzAHaZiFL6509tYOF5BG+6PpFfMbDtgT9zh+7GZ7Yg7xbvjTv9r4SRtU22cTqZs/wreO7fyXFp6Rl3FVGCcmS3Cl+gfjvKf407vl0LCeCGrPqdWfWbg3bzu6/CUj2+0stt7nnmkavwaj3I/YWabAn9rYZx+wIuxqlETSdea2WVmNlTSS620M0mSJEk6je5KUVgfz4V8ycwGsuqS9lQ84jWeSE+IyNmTwJEA4VgN78D17wMON7PBISM7Abi3WmMz2wy4GRgjaWGh6jd4hHnzaNffzKoJP88GRhSWcMcBc8Ip3BpYKmkq8H2qLJW3gqXAmjEerPyi0GHMbCNgHUl34074K/hy+vq4o/OamX2gxjUfAA4zs3XDMTu2UNem59FGaj2jB4BjzGxALPNXs7099t2CpwKcgedUl7+crQ8sDud2HzyXvM1ETvRU3En9SmH89lDOB/7fOD+5UPcIsG3k4Zbv3wZ4TvG/IzpbtukTZjbEzNYzsy0K5QfjOcX/7ICNSZIkSdJuusvBvQv4M56W8Dt8iRkASbOAIcCQOC4zFjjNzBbgb4svwJ2sNiPpTuCX+B/vBVF8fo0uE/Cl5clmNjd+Rkt6CM/1nGlm84AG4PNVrvkCnsc5PdIrxrAySnsYsMDM5uAvPZ3aznktj773xktHK9ozThW2AO6Lec4H7sSd9mnAYDP7I+5MPlypc6QP3AbMi34LC3VtfR6tpoVndBmwBHgad3YfqzJGm+2T9G88L/gY/B6VOQvPu52LP/eaqTZmdouZPVse1szujuMD8M/PcKAUn8nJtcaqYeurwLl4bm0JeKNQ90883eP/xue2BNTHZ+1gPNd4fqRk/IyVKUc3mdmCmOfpwMEddMKTJEmSpN30a2rqnX+DzGw94I2IfG2Lv4Vvkl7uWcuSJOlMSqXSMGBRXV0dAwcO7JZrdrZUb6lUor6+2mJO76ev2w99fw593X7o+3NI+3ue8hwaGxtpaGgA2Kq+vn5xe8bqzUIPuwEXxRIxwPHp3CZJ0hl0pnObJEmS9D56rYMr6R7gnublZjYT3/qoyBJJh7T1GmZ2Livfvi+yr6R/tHW8jtBTtpjZBCpvFzZe3axE1ZueRyV6o3323j2by3xH0h3dbU9foGl5E/3SwU2SJFmt6bUObjXa48jWGOs8fKuyHqenbJF0BXBFd1+3Er3peVSiN9oXTuxq4cg+f8UL9F/W9f8lbXHGJl1+jSRJkqRnSaneJEmSJEmSZLWiz0Vwk2R1xJpJBTer2xOYFOpxPUJsU/Z7XF4a4HngREmLzWw3fEeFMhvj2+Dt2M1mJkmSJAmQDm7SCzGz/qGglfQCzGyApOVmtr+kV6LsVOD/4gIW/4NLMpfb3wrMqjxakiRJknQ96eD2AczsOlzedSDwDC6UcRNwqaQZ0eYg4AxJo2Nbtavx/Unn4uIC5xekbZuPvydwMfAoLtXaBBwh6emoPxPf3xV879hTyhK8FcZq/tLTtsChkmaY2ThcVGAAvqfxSZIU0csxwGvAx4ExZrYJcAEuB/wCcIKkZ8xl3KbikcT+uKjCpBr3rgnfF/eLwFDgW6EkNwx4XNKG0e7d8/IxcDmwPzAIOBo4EZdgfhP4vKSlVa65BvBTYC9cce11SZ8Jmd3bw45BwB9iXm9VGON84AhctezBZnVteR5nA0MlnR7nQ3HRho8Au+D7+5aFH34o6YZo9yD+2dkFF2w4sOzcBkNwid/m19sYV+g7oZI9SZIkSdIdZA5u3+BUSTtJGo4rvJ1JSMMW2hxLKMEB1+LObx3uuO7cimtsB0yRtD1wI3A2gJkdgDtTu+EiA/2Bc6oNIukOSSND0nUy7ijeHXLNh+HysPXARcBVha67ABPD5udiDkeHPdNxiVpwB3mmpBHR9spWzO1VSTvHPC5pRXtwJ3SWpB3iGvcDk8OeEpV3nigzAhgNbCtpBHBQlK8AjopUgzr8Xh7XvHMogR2CR0V3AT5RqGvT88BFJ44I5xpcvW2mpDdwwZXdY45744IUGxT6fjTqDyxc/w4zWwocTmW54LHAPZL+XsOmJEmSJOlS0sHtG4w1s1Kouh2FOz63AKPMbGhE5T4L3GxmQ3DnaTqApMdpQT0rkKQ5cTwb+Fgc7w3cIOnVUKa6LMpqYmb7Ad8EDpG0DFfBGgE8GmpXF+JqaWVmSfpzHH8amCfpqTi/GhhpZoOBh4AJZvYDM9sL+Fcr5nZDYV6bmtnarejzuqTb4/gJ4NnCtmklakvu/gVYE7iyKG2L/75NjPnPxyO8Iyv0Hw38StLrkapRdOLb9DwkLcG/FJWd1PH4lyNwtb5fm1kDcDfwQXyloMz0UDArjncgsClwPfElqBnHsuoXlyRJkiTpdtLB7eVE5PMkYP+I4J4NrF2Qhj0qfmZEVK5MWyXqlhWOV9CB9BUzGwFMwZfxX4zifsBV5ehuRGCL+xlXXGJvjqSbgVG49PNZeKS3JZZF33Je7wBgOat+/ps7vY2F4xW04f7EUv52uGO9PfBkpFwcBewOjIpn+bMK1+0KpgLjzGw48AFWyiv/HE9/GB4R92eb2VPxmUh6B3e6i847ZrYL7iSvFtuWJUmSJH2XdHB7P+vj+aovmdlAVl3SnopH5MYT6QmSXsUjdkcCmNmO+FJ2e7kPONzMBoeq3ATg3mqNzWwz4GZgjKSFharf4JHozaNdfzOrpik4GxhhZuWl+XHAHEmvmdnW+Bv6U4HvA59q57yWAmvGeODOZ6dgZhsB60i6G3fCX8GX+9cHXox5fKDGNR8ADjOzdc2sPx4VLdOm5xHcAuwBnIHnLJe//KwPLA457H2oEZU2s43MbMNC0aHAgmbNjgOubR71TZIkSZLuJh3c3s9deLRyIfA7fLkcAEmz8Jd9hsRxmbHAaZHSMBF3RIovCLUaSXcCvwQeYaVDc36NLhPwpe/JZjY3fkZLegh/2Wummc0DGoDPV7nmC3h0cLqZzcdfQBsT1YcBC8xsDnApcGo757U8+t5rZn/Ao7KdxRbAfTHP+cCduNM+DRhsZn/EHf6HK3WOlwFvA+ZFv4WFurY+DwrR/mPChjJn4Xm3c/H7WiuVZRM8l3p+fK72YeUzwcwG4Xm5mZ6QJEmS9Dj9mpraupKd9HbMbD3gjYjMbYsvQ5ukl3vWsiR5L6VSaRiwqK6ujoEDB3b59bpCqrdUKlFfX21BovfT1+2Hvj+Hvm4/9P05pP09T3kOjY2NNDQ0AGxVX1+/uD1j5TZhqye7ARfFEjbA8encJonT2c5tkiRJ0vtIB3c1RNI9wD3Ny81sJrBls+Ilkg5p6zXM7FzgSxWq9pX0j7aO1xF6yhYzm0Dl7cLGF3Zc6BZ60/PorTQtf4d+AzIrK0mS5P1AOrjvI9rjyNYY6zzgvM4aryP0lC2SrgCu6O7rVqI3PY/28sJlzzJgWf8uG3+Tbw3rsrGTJEmS3kWGM5IkSZIkSZLViozgJkkvx8y+B6wnaWKFuvHAQZK+3N12NbPjO/iuCm/jkssnSHqyJ21KkiRJ3r9kBDdJWkHsR5s0w8wGmNlI4ARg55AmvhGXYk6SJEmSHiEjuEm7MLPrcFnXgcAz+Cb/NwGXSpoRbQ4CzpA0OrYruxpYF5iLiwqcH3u+Vhp/T+Bi4FFgV1yZ7QhJT0f9maxU0noMOEVSReUtMzsQ+FGhaFvgUEkzzGwccDL+u/AKcJIkRWR0DB6N/DgwJtTILgD6Ay/gUcpnzMxw0Y11om6qpElVbFkHuAZXOnsbl0g+LMa+Ht/XeG3gdknfrtB/LXz/372AF4E5hbr+wI+B/aPoLuDMgoJb87GuABZI+u84rwNm4jLNR+L7BK8VzSdKuj/aLcZV2vbC9+K9BJcmXgd4A1dLe7bSNZMkSZKkO8gIbtJeTpW0U0jOPgmcSUjCFtocSyis4ZK6l0qqwx3XnVtxje2AKZK2x6OCZwOY2QG4c7sbrtLWHzin2iCS7ihLBAOTgcdx0YJRuMDBHpLq8ahjUahgF9yxqwOeizkcHfZMB66LdicDM0N+uA6Xsa3Gfrgwx7YR7Twhyv8FHBx2jAR2MrP9K/Q/AdgKd9I/x6pKbl+NvjvGzw5RVo2pvPd5lZXO7gZ2kbQDcATulBcZIulTkr4iaR7wf4HFZva3aP+fNa6bJEmSJF1KOrhJexlrZqVQtToKd6xuAUaZ2VAzGwp8FrjZzIYAdbhTiKTHqa2aVUaSyhHK2XhkEWBv4AZJr4YzdlmU1cTM9gO+CRwiaRlwMDACeDTUvC7EVcjKzJL05zj+NDBP0lNxfjUw0swGAw8BE8zsB2a2F+6sVmMe8Ekzm2xmhwKNUd4f37t4HlDC79fICv1HA9dIejsUyn5ZqNsbd1DfkvRW2Fj1voT63WAzG25mA/CobdmR/Rj+JeBJ4FfAJhFlLvOuIpqZfQRXpdta0ma449zcIU6SJEmSbiMd3KTNROTzJGD/iOCeDaxdkIQ9Kn5mSHqj0LWtsnnLCscr6EBKjZmNAKYAn5f0YhT3A64qR3cjAlvcJ7hiykNzJN0MjMIllc/CI73V2v4Fj0zfizuf88xsbdzx3gD4dESIb8VTFbqaa4DxwAHA05L+GuXXAz+TtB0eDV7ezJ7ivTkUT3V4Ps6n4Y54kiRJkvQI6eAm7WF9PF/1JTMbiOfflpmKO0zjifQESa/iaQxHApjZjnhqQXu5DzjczAaHWtsE3GGsiJltBtwMjJG0sFD1GzwSvXm0629m1XQOZwMjzOwTcT4OmCPpNTPbGlgqaSrwfVZNG2huy+bACkm3AqcDGwEfxO/p85KWhb2frzLEA8Ax8XLXIPyLRJn7gHFmtqaZrRk2Vr0vwTT8uUxgZToJYc+iOD4Oz7WuxiJgdzNbN84PBBpauG6SJEmSdBn5klnSHu7CX8BaiL/o9BDh1EmaFSkJ5SXwMmOBq8zsP/EXkxbgTnKbkXSnmW0PPBJFjwPn1+gyAXckJ/v7YACcLum3ZvZdYGa8oLUW/qJcqcI1XzCzY4DpsZz/An4PwPN4jzazt/Ao9ak1bBkOXBh29AcukPScmV0C3GRmDfgLWvdX6X8ZsD3wNH7vHwM+VKjbmpUvnt0NXF7DFiQtMbOngD2JLyDBacCtZvYy/rxfqjHMLXgKR8nMGvEUjWNrXTdJkiRJupJ+TU1tXTVOkrZjZusBb0hqih0VHgRM0ss9a1nS05RKpWHAorq6OgYOrBUo7hhdKdVbKpWor68W/O/99HX7oe/Poa/bD31/Dml/z1OeQ2NjIw0NDQBb1dfXL27PWBnBTbqL3fCXqPrF+fHp3CbdSVc5t0mSJEnvIx3cpFuQdA9wT/NyM5sJbNmseImkQ9p6DTM7F/hShap9Jf2jreN1hF5myxR8y7MiyyXt1J129CRdGb1NkiRJeh/p4CY9Snsc2RpjnQec11njdYReZsuJPW1Da3jhij8xoLFrnNBNvrltl4ybJEmS9E4ypJEkSZIkSZKsVmQEN+k0zOx7wHqSJlaoGw8cJOnL3W1XNYr2mtmJwCBJP+lBewbi+wjvBCBpw0LdbsAkfPsugNuBb4fQRbXxFuP3vEu37DKzNYDf41K9AM8DJ0pa3JXXTZIkSZJqZAR3NSO2u0raiKQpPezcDsDFLCZRWX3sVWCcpG1xCd5dWblNWY8i6R1c9GNEyA/fiUv3JkmSJEmPsNpHcM3sOsDwjeqfwTetvwm4VNKMaHMQcIak0bGF1dXAusBcfF/R8yXdVmX8PYGLgUdxp6MJOELS01F/JnBMNH8MOEVSRYUsMzsQ+FGhaFvgUEkzzGwccDL+zF4BTpKkiIyOAV4DPg6MCUnVC/B9Vl8ATpD0jPnmq1PxSFt/XNZ1UhVb1sFVrrYD3sZlcw+Lsa8HhuDKVrdL+naF/msBlwJ74fu1zinU9Qd+DOwfRXcBZ0paUcWWYfhet5dHn0HA0cCJ+P6rb+IKZUuj/ZnAf8S9+hu+Y8NSM/sAcCUug7sU+F/g79Hne6yM5rbVvitwJa//jvM6YCYud3skvi/uWtF8oqT7o91i4Ia4RwskfQW4L+a7CsUorKRGM5sDfKSSPc04zMwuBz4MTJL007j2JFxKeS38+RxXVjGL34fvAWsC7+CO9Xwz+zQuZzwkxj5X0u1hU3FP4yHRL0mSJEl6hPdDBPdUSTuFpOyTwJm4kzeu0OZYVqo4XYs7v3W447pzK66xHTAlJFZvxKVrMbMDcOd2N3yD//7AOdUGkXRHWTYWmIw7dXeHNO5hwB6S6oGLgKsKXXfBHac64LmYw9Fhz3Tgumh3MjAzIm11uLNXjf2AIZK2jajcCVH+L+DgsGMksJOZ7V+h/wnAVriT/jlWVff6avTdMX52iLJaDAVmSdoh7L4fmBxzLAFfBzCzMbhjuYukHYE7gP+KMc4FXpX0CeDLuINXibbaN5X3fp6mRvrA3WHLDsAR+JeGIkMkfSqc21ZhZhvjDvztrWi+jqRdcSGHC2M/YoALJe0cz/Z63KHHzLYBrgCOjLpdgEVmtj4udXxUPPuDgF9EedmuO8xsKXA48I3WzidJkiRJOpv3g4M71sxKZrYAlzUdiSsvjTKzoWY2FHd0bg4FrjrcKUTS48D8VlxDksoRytm4gwW+1HyDpFfD2bmMysvPq2Bm+wHfBA6RtAw4GBgBPGpmc/Eo2haFLrMk/TmOPw3Mk/RUnF8NjDSzwbji2AQz+4GZ7YU7q9WYB3zSzCab2aFAY5T3x/eznYc7lnX4PW3OaOAaSW9L+jfwy0Ld3rgD+Jakt8LGlu7L6+VoIfAE8KykuXFewiPtAIfEWE/EvfoaMKxg05UAkl7EPweVaJN9odg22MyGR6rBkax0ZD+Gf0l5EvgVsElEwctMa2HeqxDPcSbwX4XPXC1uCBsXAy8Dm0f5AWY2O5TTJrLyGe4D3CHpT9GvUdJr+Je0rYA7477eia9WlO87kg4ENsUd5rPbMq8kSZIk6UxW6xSFiHyeBOwWUqtHAV+V9G8zm4E7vAAzJL1RlpjF/3C3hWWF4xV04L6a2Qg8UrZfOGEA/YCrJJ1bpVvFlIfmSLrZzB4B9gXOwtM1KuZxSvqLmW2HR18PAH5kZsNxx3sD4NOSlpnZZXiqQlfTWDheQfV73g9PKSlGuLuDa4DxuELb0+XlftzZO0PSrfEy1r9Z9X616tnBu2kjtwH3SPqvltoH77lPZvYR4CfAzpIWxQts01sYpx8wX9IetRpJesfMrgT+hK8YJEmSJEm3s7pHcNfH81VfijfUjyvUTcUdkvFEeoKkV/E0hiMBzGxHPLWgvdwHHG5mg0PBawJwb7XGZrYZcDMwRtLCQtVv8Ej05tGuv5lV0+ObDYwws0/E+ThgjqTXzGxrYKmkqcD3WTVtoLktmwMrJN0KnA5sBHwQv6fPh3O7GfD5KkM8ABxjZgPMbBArv0yA35dxZramma0ZNla9L21kJnCymW0Q8xgYXxrKNh0b5UOBL1YZoz32TcM/NxNYme4Cfr8WxfFxeC54mzGztfHPwewaX3RayxDgLWBpON3FfXLvAQ40s4/HdQdG1Ph/gI+b2eiCTTubWT8z28jMNiyMcSiwoIM2JkmSJEm7Wd0d3LuAPwMLgd/hS9vAu8vKQ/AcyFmFPmOB0yKlYSL+h7r4Ak2rkXQnvjT/CCv/4J9fo8sE3JGcbGZz42e0pIeA7wIzIzWggSqOpaQX8Lzf6WY2H4/QlqO0hwEL4gWlS/GXn6oxHHgkrvcH4AJJzwGXAJ+Jpe1yLmwlLgOWAE/jjuVjzerm4y+ezYnjy2vY0mokXYvnHP8u5l8CPhPVPwA2MLM/4l8kHqphe5vsk7QEeArPdS2mPpwG3GpmTwAfBV6qNY6ZPYZ/XjYws2fjBTaAr8TY+xU+G9+tNVYNWxfgL1o+hb8cuahQ9yfgeOBX8ewfAYbJZZUPAf4/M5tnZk/jL6L1AzbB0zDmx+/NPvSSHR6SJEmS9yf9mprauhq/ehMv4bwhqcl8R4UHAYs/8EmSdDKlUmkYsKiuro6BA9sV4G6RrpbqLZVK1NdXW1Tp/fR1+6Hvz6Gv2w99fw5pf89TnkNjYyMNDQ0AW9XX1y9uz1irdQ5uO9kNf4mqX5wfn85tkvRtutK5TZIkSXof6eA2Q9I9eB7iKpjZTGDLZsVLJB3S1muY2bnAlypU7SvpH20dryP0Mlum4NtSFVkuaafutKMavdE+M5tAbJHWjPGFXSbe93R1BDdJkiTpXaSD20ra48jWGOs84LzOGq8j9DJbTmy5Vc/RG+2TdAW+b22f58WrGhjQ2HK79vCh0/r2sl2SJEnSNjKkkSRJkiRJkqxWZAQ3aTex4f+ukt7saVt6CjPbCN/V4mFJX46yfXHJ5eG4Kt7EDl7jfFx57QVJo9rRfxiecnJZB2z4HiuljEcC20i6sVB/Ky4E8Q6+t+8pmSKRJEmS9BQZwU3aTcgKv++c21ArK/MzXA64yF/wLd8u6qRLngGMao9zGwyjZSnktjAS33KuyLiQgN4BmMSqUtJJkiRJ0q1kBDdpN2bWBAyW9LqZLcb3/P0csBmulLYxLvDwQeA4SQ9FNPFxXPlrH3wf1ZMlPVzjOh/C1d0+Fu0vkjQt6hbjcrT7AB8ALpb00xpj/Qn4sqR5cf51oF7SsWY2CZdtXgt4MWz+a8HmqcBe+D65U8zsaODvUXdQ+RqSnomxv9DCLSzadSC+1/HauAjD6ZJmm9nDUXa/md0t6VtmNg5XCRuA79F8kiTFOP+J3/N3gDeA3YHJwFYRcX+mHGmuYMP3iChtpfMoG4rnbA+J8R6S9A1Jxb2iPxDXT5IkSZIeISO4SWcyUNKuwH/gwghvS/oU8B18yb7MUGCepO2BU4DrQ2muGpcADdF+X+BCM6sr1G8sqR4XdPiOmW1fY6xrcGWyMseyUnnsQkk7SxqBS+z+uJnNj0naUdIUM9sUly0+q8a1WoWZfQw4Bzgg5jEBuBGgELXdLZzbUXj0dI9oexERLQ3H95BoOwI4WNI7wNeApyLiXtG5bS2SXgLOBe6L8b5RmMcVZrYE+CGr3uMkSZIk6VbSwU06k1/Fv08A6xTOS8DWhXZv4dFeJD0IvAlYjXH3Bn4R7Z/HUwJGF+qvjLq/A7fjil/VmAYcGRLCw3Ep3XL0+AAzmx0qbRPxpfgyywinM7gc+Lak12tcq7Xsh0enH4qo6HXAgIhcN+dgYATwaLS9ENgi6g4Cfi7pNXjXGe02JE2QtCX+haaz0jOSJEmSpM1kikLSmSwDkLTCzN49B1bQSz5rkpaY2ZPAAbgjPDVU6z4C/ATYWdIiM9sNmF7o+oakouzfrsCVMc/1gEFmdoekA9thVj/gLkljW9n2KknntuM6LbGcVb/0rt2eQSRda2aXmdnQ7naykyRJkgQygpv0DGvheaLEkvsg4I812t8HHB/tNwEOBB4o1I+Puo2i7rctXH8qngZwJJ6yADAEjywvNbM1gJp73kr6oKRhkobh0d472+ncgguL7G9m25ULzGznKm1/A4w1s82jXX8zK2/yehtwkpkNjrqhUf4qnhfbEs8A9Wa2RoxxUJV2q4xnZuuZ2RaF84OBf8ZPkiRJknQ7vSKqlrzveAkYaWbfxiOSR0p6q0b7bwC/MLP50f4sSU8W6l80sxLudF0gaUEL178Ff/HqMUlLACQtMLObgKfwF8zuAPZox9wws93xF9+GAP3M7AjgK5LurtRe0p/MbAweER6EfwH4PfBYhbYPmdl3gZlm1j/a3oSngUzDX/CbbWZvA6+b2R7AfECRevHHGnm4twCHA08DS2LMStwPTDSzecDv8Jzbm8xsXTxa/088/7epSv8kSZIk6VL6NTXl36Ck+yjvSCBpw04abzFwkKSGzhgv6X5KpdIwYFFdXR0DB9Z617D9dLVUb6lUor6+76ql9XX7oe/Poa/bD31/Dml/z1OeQ2NjIw0NDQBb1dfXL27PWJmikCTJak9XOrdJkiRJ7yNTFJJuRdJi4D3R21DHmlqhy08lXVFjvGGVys3scd77+Z4tqWZubVdiZlOAXZoVL5e0Uzfb0evuTVfS1dHbJEmSpPeRDm7SKwhZ15EtNmz9eN3qNLaG3uJA9sZ7A/Di1SUGNHZ+ytSHTv1Mp4/jRWBvAAAdvUlEQVSZJEmS9G4yrJEkSZIkSZKsVmQEN2k3ITSwq6Q3e9qWniK2JmsAHi7vTmBm++LKbcOBS4tSt+28xvnAl4EXCspmbek/DNhX0mUdsOF7hGxvpJNsI+nGQv0kXMFuGDA8X/pLkiRJepKM4CbtJqRa33fOrZkVvxj+DN9SrMhf8H12O0vN6wxgVHuc22AY8NVOsgU8leSwZmW34tuq/bUTr5MkSZIk7SIjuEm7MbMmYLCk12O7rl8Cn8P3Yj0L2BgXdPggcFzs4ToMeBwXWNgH39f2ZEkPv+cCK6/zIWAKLmfbD7hI0rSoW4zvObsPvg/uxZJ+WmOsPwFfljQvzr8O1Es6NqKQn8X3ln0xbP5rweapwF7AZcAUMzsa+HvUvSuKIOmZGPsLLdzCol0HAt/F1cPeAk6XNNvMHo6y+83sbknfMrNxwMn47+8rwEmSFOP8J37P3wHeAHbH9/zdKiLuz1TbB7cYpa10HmVDgfOAITHeQ5K+IWlW1Ld2ykmSJEnSZWQEN+lMBkraFV+qvhx4W9KngO/gS/ZlhgLzJG0PnAJcb2a1NkC9BGiI9vsCF5pZXaF+Y0n1wGeA75jZ9jXGugYYVzg/Frg6ji+UtLOkEcD1wI+b2fyYpB0lTTGzTYFv4o58hzCzjwHnAAfEPCYANwIUora7hXM7Co+e7hFtLwKuinHGAYdE2xG42MI7wNeApyLiXk3koVWE9O65wH0x3jc6Ml6SJEmSdAXp4Cadya/i3yeAdQrnJWDrQru38Ggvkh4E3gRqhf72Bn4R7Z/HUwJGF+qvjLq/A7cDe9YYaxpwpJkNMLPhwPpAOXp8gJnNDsWviay6q8MywukMLge+Len1GtdqLfvh0emHIip6HTAgItfNORgYATwabS8EyjK5BwE/l/QavOuMJkmSJMn7jkxRSDqTZQCSVsRS9bIoX0Ev+axJWmJmTwIH4I7wVElNZvYR4CfAzpIWmdluwPRC1zeaSc/uikvrAqwHDDKzOyQd2A6z+gF3SRrbyrZXSTq3HddpieWs+qV37S64RpIkSZJ0ORnBTXqCtfA8UWLJfRDwxxrt7wOOj/abAAcCDxTqx0fdRlH32xauPxVPAzgST1kAGIJHlpea2RpAzT1rJX1Q0rAQmpgI3NlO5xbgHmB/M9uuXGBmO1dp+xtgrJltHu36m1lZm/E24CQzGxx1Q6P8VTw/uSWeAerNbI0Y46Aq7Vo7XpIkSZL0COngJj3BS8BIM5uP70JwpKS3arT/BjAi2t8LnCXpyUL9i2ZWAh4BLpC0oIXr34JHb5+StAQg+twEPAU8Cixq+7QcM9vdzJ7Fc3RPMLNnzWy/au0l/QkYg0eE55nZ08AJVdo+hL+MNtPM5uFblH0+qqfhDvDsSF+YEc76fEBm1mBmv65h+i3AP4Gn47hUpd39wLph6yUx50tizpsD90WUPEmSJEl6hH5NTZ2vHJQk1SjvSCDpPXK97RxvMXBQ7rvadymVSsOARXV1dQwcWOtdw/bRHVK9pVKJ+vr6lhv2Uvq6/dD359DX7Ye+P4e0v+cpz6GxsZGGhgaArerr6xe3Z6yM4CZJslrT1c5tkiRJ0vvoFS/+JO8fJC0G3hO9DXWsqRW6/FTSFTXGG1ap3Mwe572f79mSaubWdiVmNgXYpVnxckk7dbMdve7edBVNy1fQb0D/njYjSZIk6WbSwU16BZLmsuq2XB0dr1udxtbQWxzI3nhvAF665n8Y0Ni5KVMbn7JXp46XJEmS9A1y7S5JkiRJkiRZrcgIbpL0AirJ4hbqxuMv0nVIhawjhNLcDGAngOJLgrFn8CRcNANcbOPbzfYNTpIkSZJuIyO4SRKYWSZrVsDMBuBiHZNwVbnmvAqMk7QtsAMugjGm+yxMkiRJklXJCG5SFTO7DpfQHYiLAByH7xV7qaQZ0eYg4AxJo81sW+BqYF1gLi7Pe76k26qMvydwMb7v7K5AE3CEpKej/kzgmGj+GHBKNWlcMzsQ+FGhaFvgUEkzzGwccDL+eX8FOEmSIjI6BngN+DgwJoQkLgD6Ay8AJ0h6xlyybCouQdwfV0CbVMWWdXABie2AtwFJOizGvh4XlVgbuF3Styv0Xwu4FNgLeBGYU6jrD/wY2D+K7gLOlLSiii1XAAsk/Xec1wEzcWngI4FTceENgImS7o92i4EbwoYFkr6C7287rPk1ilu0SWo0sznARyrZkyRJkiTdQUZwk1qcKmknScOBJ4EzcSdvXKHNsbhTC3At7vzW4Y5rNTWuItsBUyRtD9wInA1gZgfgzu1uwHDcqTyn2iCS7pA0UtJIYDLwOHB3KKUdBuwhqR64CLiq0HUX3LGrA56LORwd9kwHrot2JwMzJY2ItlfWmNN+wBBJ20oawUrRhn8BB4cdI4GdzGz/Cv1PALbCnfTPAZ8q1H01+u4YPztEWTWm8t7nNTXSB+4GdpG0A3AEK1XdygyR9KlwbluFmW0M/AeeppAkSZIkPUJGcJNajDWzo/EI37rAQuA84CcFGdjPRrshQB3uFCLp8VAeawlJKkcoZwMHx/HewA2SXgUws8uA/25psFAM+yYwStIyMzsYGAE86kFY+gEbFLrMkvTnOP40ME/SU3F+NfCzkK19CPg/EZ39LbXlgOcBnzSzycCDrHT2+gMXRc5qP2AT3Fm9q1n/0cA1kt4G3jazXwK7F+7L1LLym5ldDXwR+HklQyTNMrPBZjYcVyg7Eo+Wg0dxrzezzfBI8yZmtomkpVE/rcYc30Pcp5nAfxWeaZIkSZJ0OxnBTSoSkc+TgP0jgns2sLakf+MvGx0VPzMkvVHo2tYXi5YVjlfQgS9dZjYCmAJ8XtKLUdwPuKoc3Y0I7JaFbhVTHpoj6WZgFPBn4Cw80lut7V/wyPS9uEM6z8zWxh3vDYBPR4T4VjxVoau5BhgPHAA8LemvUX498DNJ2+HR4OXN7GnVvYF30zJuA+6R9F+dYXSSJEmStJd0cJNqrI/nq74Ub9AfV6ibijtM44n0hIi0PolHCDGzHfHUgvZyH3B4RB/7ARNwh7EiEYW8GRgjaWGh6jd4hHnzaNffzKppGc4GRpjZJ+J8HDBH0mtmtjWwVNJU4PusmjbQ3JbNgRWSbgVOBzYCPojf0+cjsrwZ8PkqQzwAHGNmA8xsEP5Fosx9wDgzW9PM1gwbq96XYBr+XCawMp2EsGdRHB+H51q3mXDef4OLRZzbnjGSJEmSpDNJBzepxl14tHIh8DvgiXKFpFn4i1JD4rjMWOA0M1sATAQW4E5ym5F0J/BL4JEYB+D8Gl0m4I7kZDObGz+jJT0EfBeYaWbzgAaqOJaSXsDzfqdHesUYVu4GcBiwIF6guhR/Oasaw4FH4np/AC6Q9BxwCfAZM2vAc3jvr9L/MmAJnlLwAP6CXbFuPv7i2Zw4vryGLUhaAjwF7AncUqg6DbjVzJ4APgq8VGscM3sMfx4bmNmz8QIbwFdi7P0K9/67tcZKkiRJkq6kX1NTblWZdA5mth7whqSm2FHhQcAkvdyzliW9mVKpNAxYVFdXx8CB7QoiV6W7pHpLpRL19dUWBno/fd1+6Ptz6Ov2Q9+fQ9rf85Tn0NjYSENDA8BW9fX1i9szVr5klnQmu+EvUfWL8+PTuU16ku5wbpMkSZLeRzq4Sach6R7gnublZjYT2LJZ8RJJh7T1GmZ2LvClClX7SvpHW8frCL3Mlin4lmdFlkvaqTvtSJIkSZLeQDq4SZfTHke2xljn4VuV9Ti9zJYTe9qG3kZ3pSckSZIkvY90cJMk6RW8NO23DGh8p9PG2/jrB3baWEmSJEnfIh3cJOkFhGzwQZK+XKFuT2BST6cbhHTzaODDwOCybLKZbQP8IsqX47s+nCzpzZ6yNUmSJHl/k9uEJb0OM8t15V6EmZW/CF+JK6815y3gm5I+AWwPrINvE5ckSZIkPUJGcPsAETkzfCP+Z/BN+W8CLpU0I9ocBJwhaXRs0XU1Lq87F9gaOF/SbVXG3xO4GHgUl3FtAo6Q9HTUn4nvDwsenTulHL2rMNaBwI8KRdsCh0qaYWbjgJPxz90rwEmSFNHLMcBrwMeBMWa2CXABLm/7AnCCpGfM9Xan4k5Uf1y2dlKNe9eE74P7RWAo8C1JN5vZMOBxSRtGu3fPy8f4/rL7A4OAo4ETcTnfN3G1tKVUwMzWAH4K7AU0Aq9L+kw4ireHHYPwPXJPKMvuNhvjfOAI4GV8u7ViXVuex9nAUEmnx/lQQMBH8JfSzsfVywYAP5R0Q7R7EP/s7AL8EzhQ0gNRt8o1JC0GFsfxO2b2B+CTlexJkiRJku4gI7h9g1Ml7RSSuU8CZ+JO3rhCm2NZqVJ1Le781uGO686tuMZ2wJSQkL0Rl+bFzA7AnandcAGD/sA51QaRdEdZFheYjDuKd4f072HAHpLqgYuAqwpddwEmhs3PxRyODnumA9dFu5OBmSG5W4dHFVviVUk7xzwuaUV7cCd0lqQdWCnKMDnsKQFfr9F3BL6Uv62kEcBBUb4COCpSDerwe3lc885mdjBwCB4t3QX4RKGuTc8DVzE7ohCFPQq/f2/g4h27xxz3BiaZ2QaFvh+N+lYns4by2nHAzNb2SZIkSZLOJh3cvsFYMyuFQthRuONzCzDKzIZGVO6zwM1mNgR3nqYDSHocV7tqCUmaE8ezgY/F8d7ADZJeldSEK2nt3dJgZrYf8E3gEEnLgINxx+9RM5sLXAhsUegyS9Kf4/jTwDxJT8X51cBIMxsMPARMMLMfmNlewL9aMbcbCvPaNKRlW+J1SbfH8RPAs5LmxnkJj4pX4y/AmsCVZnZMoXwNYGLMfz4e4a205D8a+JWk1yWtYFUnvk3PI1TMngTKTup4/MsRuPLbr0NZ7W5cTrgYnp0uaXmNea5CONE3AA9ISgc3SZIk6THSwe3lROTzJGD/iOCeDawt6d/ADNzhPQqYEVG5Mm2VqFtWOF5BB9JXzGwEMAVfxn8xivsBV5WjuxGBLe6NW3GJvTmSbgZG4TLCZ+GR3pZYFn1XxPkA/GWo4ue/udPbWDheQRvuj6RX8Ij4DXhO6pORcnEUsDswKp7lzypctyuYCowzs+HAB4CHo/znePrD8Ii4P9vMnlY9E3g3b/o6PKXiGx03OUmSJEnaTzq4vZ/18XzVl8xsIKsuaU/FI3LjifQESa/iEbsjAcxsR3wpu73cBxxuZoNDoWwCcG+1xma2GXAzMEbSwkLVb/BI9ObRrr+ZVdMUnA2MMLPy0vw4YI6k18xsa2CppKnA94FPtXNeS4E1Yzxw57NTMLONgHUk3Y074a/gy/3rAy/GPD5Q45oPAIeZ2brhOB5bqGvT8whuAfYAzsBzlstfftYHFoe08j7UjkpXJXKOp+KO/1cK4ydJkiRJj5AObu/nLjxauRD4Hb5cDoCkWcAQYEgclxkLnBYpDROBBbiT1WYk3Qn8EngkxgF/MakaE/Cl78lmNjd+Rkt6CH/Za6aZzQMagM9XueYLeJ7pdDObj7+ANiaqDwMWmNkc4FLg1HbOa3n0vTdeilrRQpe2sAVwX8xzPnAn7rRPAwab2R9xh//hSp3jZcDbgHnRb2Ghrq3Pg0K0/5iwocxZeN7tXPy+1kxlMbNbzOzZ8rBmdnccH4A/n+FAKZ755FpjJUmSJElX0q+pKYMtqxtmth7wRkTmtsWXoU3Syz1rWZK8l1KpNAxYVFdXx8CBAztt3O5UMiuVStTXV1uQ6P30dfuh78+hr9sPfX8OaX/PU55DY2MjDQ0NAFvV19cvbs9YuU3Y6sluwEWxhA1wfDq3yfuNlOlNkiR5/5IO7mqIpHuAe5qXm9lMYMtmxUskHdLWa5jZucCXKlTtK+kfbR2vI/SULWY2gcrbhY0v7LjQLfSm55EkSZIkPU06uO8j2uPI1hjrPOC8zhqvI/SULZKuAK7o7utWojc9j95Cd6YoJEmSJL2LdHCTJOkVvHTtXQxo7Lx3/Tb+WqWAdpIkSfJ+IB3cJHmfYma3AlsB7+B73p5STq0ws22Aa3BFt5eAsZL+1MJ45+Bb1oFvR/aDLjI9SZIkSWqS24QlyfuMgmzvuBDc2AGYxKrSyVNwaeJtcMnlX7Qw5h7AobiKXh1waJQlSZIkSbeTEdwkaSdm1oTv7ftFPNL5LUk3m9kw4HFJG0a7d8/Lx8DlwP7AIOBo4ERcovhNXAFuaZVrjgH+Q9IX43wAsAT4DLAero62Lq5Idpmki6PdVFy9zYDBwMhQXCvzATySi5ltDOwI7BN11wM/NbONYo/iShwOTJP0ZowxLcoeqnkTkyRJkqQLyAhuknSMVyXtjIsoXNLKPkOBWRE5vRK4H4+Wbg+UqLwzQ5lbgFFmtmGcHwD8UdIiYDGwt6QdcYW3r5rZJwt9R+KSzyPLBWZ2hZktAX6IK8aBC1X8rSxtHP8+F+XV2BL4a+F8SQvtkyRJkqTLSAc3STrGDfHvbGBTM1u7FX1el3R7HD8BPFvYVqxEDcncUCW7lZUyv+NxmVyAdYArQ8Hu98CmwIhC919LeqPZeBMkbQl8B7ioFbYnSZIkSa8nHdwk6RjL4N0oJ3jaz3JW/d1q7vQ2Fo5XlMconLeUOjQVGGdmQ4HPAr+O8h8BS4EdJI0A/tDs2q9XG1DStcDoGPN/gc3MrD9A/LtplFdjCfCRwvmWLbRPkiRJki4jHdwk6XyWAmuaWTkSe1Stxm1F0ixgCHABcGtEdQHWB/5X0nIzqwNGVRvDzNYzsy0K5wcD/wT+GcIQc4Ejo/pIYE6N/FuAm4CxZjbIzAYBY4Eb2zfDJEmSJOkY+ZJZknQy4WCeCtxrZi8At7fUpx1cA/yAVZ3Y84FrzewrwEJqv+C1LnCTma2LR43/CRwsqSnqTwSuCYW0l3GHtSqSHjSzW4Ano2iapN+1cU5JkiRJ0in0a2pqarlVkiRJF1EqlT4GPLPNNtuw1lprddq4TStW0K9/9yiZNTQ0UFdX1y3X6gr6uv3Q9+fQ1+2Hvj+HtL/nKc/hrbfeYuHChQBb19fX/7k9Y6WDmyRJj1IqlXYHHu5pO5IkSZJex6j6+vpZ7emYKQpJ0gsxswlU3i5sfGHHhW4l9se9p0LVLZLO68DQj+GpFs/j6RJJkiTJ+5v+wIfxvw/tIiO4SZIkSZIkyWpF7qKQJEmSJEmSrFakg5skSZIkSZKsVqSDmyRJkiRJkqxWpIObJEmSJEmSrFakg5skSZIkSZKsVqSDmyRJkiRJkqxWpIObJEmSJEmSrFak0EOSJF2CmW0DXAMMBV4Cxkr6U7M2/YFLgP2BJuBCSVe0VNdddMIc9gV+BAwHLpU0sRvN7wz7zwGOwAU43ga+I+nuPmT/scDpwDv4xvGXS7qku+wPGzo0h0IbA+YAP+vOz1EnPIPvAScDz0Xz30v6WvdY/659HX4GZnYYcA7QL+r3lvT3vmC/mU0Dti803x74gqSZ3WB+Z9i/MXA1sAWwJvBb4BuSlte6bkZwkyTpKqYAkyVtA0wGflGhzdHA1sDHgV2B75nZsFbUdRcdncNfgAnARV1vakU6av8fgJ0lbQ8cB/zKzAZ1udUr6aj9NwMjJI0EdgPOMLPtK4zRlXR0DuU//r8Abu1ya99Lh+0HpkkaGT/d6twGHZqDme0EfA/YR1IdsDvwSteb/S4dsl/S2PL9B8YBLwPd9kWVjn+GvgM8Hf8PbQ/UA19q6aLp4CZJ0unEN+4dgeuj6HpgRzPbqFnTw/Go2juSXsD/gB/airoupzPmIOmZkFauGWnoCjrJ/rsl/TvazcejV0O73Hg6zf5XJZXlOtfBoz/dJt/ZSb8HAGcBtwELu9jkVehE+3uMTprD6cAkSUsBJL0iaVnXW98lz+ArwHWSGrvK5iKdZH8TMNjM1gAGAmsBf2vp2ungJknSFWwB/E3SCoD497koL7Il8NfC+ZJCm1p13UFnzKEn6Wz7xwJ/lvRsF9haiU6x38wOMbMno81FkhZ0qdWr0uE5mNkIYD/gJ11u7XvprM/QEWY238zuMbNdu9LgCnTGHLYFPmpmD5nZE2Z2tpn162K7y3Ta77GZrQUcBVzVZda+l86w/wfANsDzwFLgbkm/b+nC6eAmSZIkNTGzz+J/ZI7saVvaiqSZkrbD/0AeE7msfQIzWxO4DDix7CD0QaYAW8Xy8kXADDPrllWATqQ/vjS+D/BZ4ADgmB61qH18AVgSq0p9iUPxFaQPA5sBe5jZl1vqlA5ukiRdwf8Cm0XuYDmHcNMoL7IE+EjhfMtCm1p13UFnzKEn6RT7I+L2S/ylFHWpxavSqfdf0hI8p/igLrG2Mh2dw4eBjwF3mNli4DTgeDO7rGvNfpcOPwNJSyW9Hcf3RnldF9tdpLP+L/q1pEZJrwEzgE91qdUr6czfg+Po3ugtdI79p+BpFe9IegW//6NbunA6uEmSdDqS/gHMZWXE70hgTuRWFbkJ/4O9RuRkfQH4dSvqupxOmkOP0Rn2m9nOwK+AL0t6onssdzrJ/k+WG5nZhvgfxW5LUejoHCQtkbShpGGShgEX43mKX+0L9gOY2WblRmY2EhgGdNsXpU76PZ4O7Gtm/SKq/jlgXtdb33n/D5nZ5sAo4Lqut3olnWT/Iv5fe/cX6nddx3H8ORq6aHOxFYQ1Jya+oG6iVYo6EPRGUUMxlGqabQ0vhg1KIZUZhqYXCyYjDBE31twiKCg30hslK2KKrNqS97zQtVEbpcMQnXO5Lj6fX/zase3IL8/O+fF8wOF8/3y+n8/7e87v4s3n9/nTVlcYDLO4DNh1srZdJkzS++UWYGOSNbRZuzcCJNkOrKmq54BNwPnAYMmYe6rqpX58ontTZaR3SHIxsBU4A5iV5AZgeU3dUluj/g9+CHwQ+NHQN/vLpnAc66jxr0xbqu1t2gS59VX15BTFPjDqO5xqo8Z/X5IltKXmjtA+Pwem8gUY/R22Ap8D/kxbcu4J4JGpC///8hm6CfhlVR2aurD/Y9T4VwMPJfkTbbjIU8DDJ2t01rFjUzahVJIkSXrfOURBkiRJY8UEV5IkSWPFBFeSJEljxQRXkiRJY8UEV5IkSWPFBFeSJEljxQRXkiQgyctJLpsGcTydZMWpjkOaydzoQZKkaSDJLNqGFJJG5EYPkiTRenCBFcAngG8AO4CbgVeBrwLnAd8DTgduq6qN/bkNwGHgk8AFwPPAjVW1t9+/EFjXn98DfLOqftfvPQ38FrgE+CzwM+AG2u5rR4ENVbUqyTrgWmA+bben1VX1TK/ju8CnegzXAH8Bbuo7RJFkUW9/Ke2b2y1Vtarf+zpwG/Cx/r4rB3FLM5lDFCRJmuh84I/AQuAx2natnwfOpSW765PMHSr/FVry+xFgJ7AZIMkCYBvwYK/rB8C2JAuHnl0GrATmAV8DngFWVdXcQSIKPAt8BljQ4/lpkjlDdVzdY/ww8AtgfW//A8DjwF7gbODjvRxJvgjcQUucP9rb3fLe/1TS9OMQBUmSJnqpqh4FSPIT4E7gnqp6C3gyyRFasruzl99WVb/u5e8EXus9p5cAL1bVpl5uS5JbgauADf3ahqraPWg4yYRgqurHQ6drk9wFBPhDv/abqtren98ErO7XvwCcSetxPjoo23/fAny/ql7oz90H3JFksb24mulMcCVJmujg0PGbAFV1/LXhHtx9g4Oqej3Jq7TE8kxa7+mwvbSe1AnP/i9Jvg0s7/UdA86g9RYPHBg6fgOYk2Q2sAjYO5TcDlsMrEuydujarB6bCa5mNBNcSZJGt2hw0IcuLAD+2n8WH1f2LOBXQ+fHT4b5r/MkS4HbgUuB3VX1TpJDTG5C2j7grCSz3yXJ3QfcW1WbJ1GPNKM4BleSpNFdkeTiJKfRxuL+vqr2AduB85J8OcnsJNfTJoQ9foK6DgLnDJ3Po004+zswO8kaWg/uZOwA/gbcn+RDSeYkuajfewj4TpJPAySZn+RLk6xXmtZMcCVJGt1jwN20FReW0CaiUVWvAFcC3wJeofXEXllV/zhBXeuA65IcSvIg8AStx3cPbejAYSYxrKG3/y/aeN9zaasr7Aeu7/d+DjwAbE3yT2AXcPnkX1mavlwmTJKkEfRlwvZX1V2nOhZJjT24kiRJGismuJIkSRorDlGQJEnSWLEHV5IkSWPFBFeSJEljxQRXkiRJY8UEV5IkSWPFBFeSJEljxQRXkiRJY+XfENxCRwdOtYAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 504x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XxwkRX5gnXWi",
        "colab_type": "text"
      },
      "source": [
        "We will create new datasets with less features."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CfArYVshrZNh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1eb3dd0f-8a49-4151-d7b6-9a0c6a850540"
      },
      "source": [
        "#getting top k features\n",
        "top=250\n",
        "features = get_top_features(X_train,model.feature_importances_,top=top)\n",
        "print(\"Taking top %i features\"%(top))\n",
        "X_train_200 = X_train[features].copy()\n",
        "X_val_200 = X_val[features].copy()\n",
        "X_test_200 = X_test[features].copy()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Taking top 250 features\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "xA9bSzUARRZ_"
      },
      "source": [
        "# XGB Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "3UWwclfyRRaC",
        "colab": {}
      },
      "source": [
        "model = xgb.XGBClassifier(n_jobs=-1,\n",
        "                      nthread=-1,\n",
        "                      scale_pos_weight=1,\n",
        "                      learning_rate=0.01,  \n",
        "                      colsample_bytree = 0.6,\n",
        "                      subsample = 0.9,\n",
        "                      objective='binary:logistic', \n",
        "                      n_estimators=1000,\n",
        "                      reg_alpha = 0.3,\n",
        "                      max_depth=5, \n",
        "                      gamma=0, random_state=42\n",
        "                     )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T0CEyW37Dai9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "72818835-0147-44ad-a725-921626b7ae64"
      },
      "source": [
        "#https://towardsdatascience.com/fine-tuning-xgboost-in-python-like-a-boss-b4543ed8b1e\n",
        "\"\"\"\n",
        "manually changed each parameters 1 by 1 and compared the validation auc\n",
        "and selected the best parameter value\n",
        "\"\"\"\n",
        "eval_metric = ['auc','error']\n",
        "%time model.fit(X_train,y_train,eval_set=[(X_val,y_val)],eval_metric=eval_metric,verbose=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0]\tvalidation_0-auc:0.769349\tvalidation_0-error:0.040253\n",
            "[1]\tvalidation_0-auc:0.810675\tvalidation_0-error:0.039551\n",
            "[2]\tvalidation_0-auc:0.819726\tvalidation_0-error:0.039551\n",
            "[3]\tvalidation_0-auc:0.818867\tvalidation_0-error:0.039551\n",
            "[4]\tvalidation_0-auc:0.822463\tvalidation_0-error:0.039551\n",
            "[5]\tvalidation_0-auc:0.824137\tvalidation_0-error:0.039551\n",
            "[6]\tvalidation_0-auc:0.822496\tvalidation_0-error:0.039551\n",
            "[7]\tvalidation_0-auc:0.823972\tvalidation_0-error:0.039551\n",
            "[8]\tvalidation_0-auc:0.827108\tvalidation_0-error:0.039551\n",
            "[9]\tvalidation_0-auc:0.825815\tvalidation_0-error:0.039551\n",
            "[10]\tvalidation_0-auc:0.827207\tvalidation_0-error:0.039551\n",
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            "[997]\tvalidation_0-auc:0.846949\tvalidation_0-error:0.039639\n",
            "[998]\tvalidation_0-auc:0.846942\tvalidation_0-error:0.039639\n",
            "[999]\tvalidation_0-auc:0.846961\tvalidation_0-error:0.039639\n",
            "CPU times: user 18min 29s, sys: 2.44 s, total: 18min 32s\n",
            "Wall time: 9min 32s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "              colsample_bynode=1, colsample_bytree=0.6, gamma=0,\n",
              "              learning_rate=0.01, max_delta_step=0, max_depth=5,\n",
              "              min_child_weight=1, missing=None, n_estimators=1000, n_jobs=-1,\n",
              "              nthread=-1, objective='binary:logistic', random_state=42,\n",
              "              reg_alpha=0.3, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
              "              silent=None, subsample=0.9, verbosity=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "q3LRv6T8RRaN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b23753b7-7baa-4660-c25b-172760ab4bed"
      },
      "source": [
        "y_pred = model.predict_proba(X_val)[:,1]\n",
        "label=\"XGBoost Classifer\"\n",
        "labels.append(label)\n",
        "y_preds.append(y_pred)\n",
        "i+=1\n",
        "result = plot_auc(y_val,y_pred,label)\n",
        "result_df = result_df.append(result)\n",
        "del result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\t\t XGBoost Classifer on Normal with OHE dataset \t\t \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The log loss and auc was found to be 0.133 and 0.847 for XGBoost Classifer on Normal with OHE dataset.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "qUwaU5nuRRaR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c0b10a68-5da1-45f4-dbc5-9272e53789a3"
      },
      "source": [
        "result_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Dataset</th>\n",
              "      <th>log_loss</th>\n",
              "      <th>auc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logisic Regression</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.518785</td>\n",
              "      <td>0.824362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.149245</td>\n",
              "      <td>0.771045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.137304</td>\n",
              "      <td>0.832997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>XGBoost Classifer</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.132829</td>\n",
              "      <td>0.846961</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      Model          Dataset  log_loss       auc\n",
              "0        Logisic Regression  Normal with OHE  0.518785  0.824362\n",
              "1  Decision Tree Classifier  Normal with OHE  0.149245  0.771045\n",
              "2  Random Forest Classifier  Normal with OHE  0.137304  0.832997\n",
              "3         XGBoost Classifer  Normal with OHE  0.132829  0.846961"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "LeV2q75ULzvm"
      },
      "source": [
        "# LightGBM Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "32dzyVRQLzvn",
        "colab": {}
      },
      "source": [
        "model = lgb.LGBMClassifier(n_jobs=-1,\n",
        "                      nthread=-1,\n",
        "                      scale_pos_weight=1,\n",
        "                      learning_rate=0.01,  \n",
        "                      colsample_bytree = 0.6,\n",
        "                      subsample = 1,\n",
        "                      objective='binary', \n",
        "                      n_estimators=348,\n",
        "                      reg_alpha = 0.2,\n",
        "                      max_depth=6, \n",
        "                      gamma=0, random_state=42\n",
        "                     )\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "yJS6sV0qLzvr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c142ec3-0212-4acf-d92d-8d03c528423e"
      },
      "source": [
        "#https://towardsdatascience.com/fine-tuning-xgboost-in-python-like-a-boss-b4543ed8b1e\n",
        "\"\"\"\n",
        "manually changed each parameters 1 by 1 and compared the validation auc\n",
        "and selected the best parameter value\n",
        "\"\"\"\n",
        "eval_metric = ['auc','error']\n",
        "%time model.fit(X_train,y_train,eval_set=[(X_val,y_val)],eval_metric=eval_metric,verbose=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1]\tvalid_0's binary_logloss: 0.165829\tvalid_0's auc: 0.806966\n",
            "[2]\tvalid_0's binary_logloss: 0.165069\tvalid_0's auc: 0.825192\n",
            "[3]\tvalid_0's binary_logloss: 0.164416\tvalid_0's auc: 0.827955\n",
            "[4]\tvalid_0's binary_logloss: 0.163786\tvalid_0's auc: 0.829187\n",
            "[5]\tvalid_0's binary_logloss: 0.163168\tvalid_0's auc: 0.828736\n",
            "[6]\tvalid_0's binary_logloss: 0.162515\tvalid_0's auc: 0.830973\n",
            "[7]\tvalid_0's binary_logloss: 0.161909\tvalid_0's auc: 0.831507\n",
            "[8]\tvalid_0's binary_logloss: 0.161343\tvalid_0's auc: 0.832969\n",
            "[9]\tvalid_0's binary_logloss: 0.160846\tvalid_0's auc: 0.832153\n",
            "[10]\tvalid_0's binary_logloss: 0.160304\tvalid_0's auc: 0.833458\n",
            "[11]\tvalid_0's binary_logloss: 0.159829\tvalid_0's auc: 0.833088\n",
            "[12]\tvalid_0's binary_logloss: 0.15931\tvalid_0's auc: 0.833762\n",
            "[13]\tvalid_0's binary_logloss: 0.158832\tvalid_0's auc: 0.834666\n",
            "[14]\tvalid_0's binary_logloss: 0.158389\tvalid_0's auc: 0.834573\n",
            "[15]\tvalid_0's binary_logloss: 0.15801\tvalid_0's auc: 0.834009\n",
            "[16]\tvalid_0's binary_logloss: 0.157616\tvalid_0's auc: 0.833936\n",
            "[17]\tvalid_0's binary_logloss: 0.157133\tvalid_0's auc: 0.835374\n",
            "[18]\tvalid_0's binary_logloss: 0.156668\tvalid_0's auc: 0.836051\n",
            "[19]\tvalid_0's binary_logloss: 0.156252\tvalid_0's auc: 0.83603\n",
            "[20]\tvalid_0's binary_logloss: 0.155834\tvalid_0's auc: 0.836595\n",
            "[21]\tvalid_0's binary_logloss: 0.155439\tvalid_0's auc: 0.836991\n",
            "[22]\tvalid_0's binary_logloss: 0.155083\tvalid_0's auc: 0.836317\n",
            "[23]\tvalid_0's binary_logloss: 0.154778\tvalid_0's auc: 0.836085\n",
            "[24]\tvalid_0's binary_logloss: 0.154475\tvalid_0's auc: 0.835837\n",
            "[25]\tvalid_0's binary_logloss: 0.154092\tvalid_0's auc: 0.836484\n",
            "[26]\tvalid_0's binary_logloss: 0.153796\tvalid_0's auc: 0.83608\n",
            "[27]\tvalid_0's binary_logloss: 0.153502\tvalid_0's auc: 0.83536\n",
            "[28]\tvalid_0's binary_logloss: 0.153155\tvalid_0's auc: 0.835316\n",
            "[29]\tvalid_0's binary_logloss: 0.152851\tvalid_0's auc: 0.835294\n",
            "[30]\tvalid_0's binary_logloss: 0.152529\tvalid_0's auc: 0.835155\n",
            "[31]\tvalid_0's binary_logloss: 0.152206\tvalid_0's auc: 0.834945\n",
            "[32]\tvalid_0's binary_logloss: 0.151884\tvalid_0's auc: 0.835112\n",
            "[33]\tvalid_0's binary_logloss: 0.151572\tvalid_0's auc: 0.835336\n",
            "[34]\tvalid_0's binary_logloss: 0.151298\tvalid_0's auc: 0.834897\n",
            "[35]\tvalid_0's binary_logloss: 0.151058\tvalid_0's auc: 0.835381\n",
            "[36]\tvalid_0's binary_logloss: 0.150826\tvalid_0's auc: 0.835406\n",
            "[37]\tvalid_0's binary_logloss: 0.150589\tvalid_0's auc: 0.835246\n",
            "[38]\tvalid_0's binary_logloss: 0.150323\tvalid_0's auc: 0.835191\n",
            "[39]\tvalid_0's binary_logloss: 0.150054\tvalid_0's auc: 0.835238\n",
            "[40]\tvalid_0's binary_logloss: 0.149781\tvalid_0's auc: 0.835293\n",
            "[41]\tvalid_0's binary_logloss: 0.14951\tvalid_0's auc: 0.835754\n",
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            "[348]\tvalid_0's binary_logloss: 0.133481\tvalid_0's auc: 0.846349\n",
            "CPU times: user 1min 9s, sys: 368 ms, total: 1min 10s\n",
            "Wall time: 37.2 s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=0.6,\n",
              "               gamma=0, importance_type='split', learning_rate=0.01,\n",
              "               max_depth=6, min_child_samples=20, min_child_weight=0.001,\n",
              "               min_split_gain=0.0, n_estimators=348, n_jobs=-1, nthread=-1,\n",
              "               num_leaves=31, objective='binary', random_state=42,\n",
              "               reg_alpha=0.2, reg_lambda=0.0, scale_pos_weight=1, silent=True,\n",
              "               subsample=1, subsample_for_bin=200000, subsample_freq=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "k_CEadVqLzvy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9acff619-6868-4a75-b0d2-e3116e54a297"
      },
      "source": [
        "y_pred = model.predict_proba(X_val)[:,1]\n",
        "label=\"LightGBM Classifer\"\n",
        "labels.append(label)\n",
        "y_preds.append(y_pred)\n",
        "i+=1\n",
        "result = plot_auc(y_val,y_pred,label)\n",
        "result_df = result_df.append(result)\n",
        "del result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\t\t LightGBM Classifer on Normal with OHE dataset \t\t \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The log loss and auc was found to be 0.133 and 0.846 for LightGBM Classifer on Normal with OHE dataset.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "BF22prvpLzv1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1e80b4e-f418-4d0c-d8cb-2cccc9098091"
      },
      "source": [
        "result_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Dataset</th>\n",
              "      <th>log_loss</th>\n",
              "      <th>auc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logisic Regression</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.518785</td>\n",
              "      <td>0.824362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.149245</td>\n",
              "      <td>0.771045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.137304</td>\n",
              "      <td>0.832997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>XGBoost Classifer</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.132829</td>\n",
              "      <td>0.846961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LightGBM Classifer</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.133481</td>\n",
              "      <td>0.846349</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      Model          Dataset  log_loss       auc\n",
              "0        Logisic Regression  Normal with OHE  0.518785  0.824362\n",
              "1  Decision Tree Classifier  Normal with OHE  0.149245  0.771045\n",
              "2  Random Forest Classifier  Normal with OHE  0.137304  0.832997\n",
              "3         XGBoost Classifer  Normal with OHE  0.132829  0.846961\n",
              "4        LightGBM Classifer  Normal with OHE  0.133481  0.846349"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "LECmbx42-V2m"
      },
      "source": [
        "# Random Forest (top 250)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "CUgv74lV-V2n",
        "colab": {}
      },
      "source": [
        "model = ensemble.RandomForestClassifier(class_weight='balanced')\n",
        "params = { 'n_estimators':[1000,2000],\n",
        "          'max_depth':[500,1000], \n",
        "         'min_samples_split':[100,500],\n",
        "          'min_samples_leaf':[3,5],\n",
        "          'max_leaf_nodes':[100,250] \n",
        "          }\n"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "eCt8L0AV-V2q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "0a3ef090-d167-459b-95e4-000e8b7c579c"
      },
      "source": [
        "%%time\n",
        "find_best_params(model,params,cv=3,X_train=X_train_200)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  30 out of  30 | elapsed: 61.8min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "The best auc score was 0.820\n",
            "The best params were: {'n_estimators': 2000, 'min_samples_split': 500, 'min_samples_leaf': 5, 'max_leaf_nodes': 250, 'max_depth': 500}\n",
            "CPU times: user 5min 27s, sys: 829 ms, total: 5min 28s\n",
            "Wall time: 1h 7min 16s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight='balanced',\n",
              "                       criterion='gini', max_depth=500, max_features='auto',\n",
              "                       max_leaf_nodes=250, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=5, min_samples_split=500,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=2000,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "DVdFANOy-V2u",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "61622725-fdda-41a3-a283-2160c74595e3"
      },
      "source": [
        "model = ensemble.RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight='balanced',\n",
        "                       criterion='gini', max_depth=500, max_features='auto',\n",
        "                       max_leaf_nodes=250, max_samples=None,\n",
        "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
        "                       min_samples_leaf=3, min_samples_split=500,\n",
        "                       min_weight_fraction_leaf=0.0, n_estimators=1000,\n",
        "                       n_jobs=-1, oob_score=False, random_state=None,\n",
        "                       verbose=0, warm_start=False)\n",
        "\n",
        "model.fit(X_train_200,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight='balanced',\n",
              "                       criterion='gini', max_depth=500, max_features='auto',\n",
              "                       max_leaf_nodes=250, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=3, min_samples_split=500,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=1000,\n",
              "                       n_jobs=-1, oob_score=False, random_state=None, verbose=0,\n",
              "                       warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "okg4Xksh-V2y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "67e44af4-99a8-4139-b83c-8cc8512b9d2a"
      },
      "source": [
        "cc_model = calibration.CalibratedClassifierCV(model,cv='prefit')\n",
        "cc_model.fit(X_train_200,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CalibratedClassifierCV(base_estimator=RandomForestClassifier(bootstrap=True,\n",
              "                                                             ccp_alpha=0.0,\n",
              "                                                             class_weight='balanced',\n",
              "                                                             criterion='gini',\n",
              "                                                             max_depth=500,\n",
              "                                                             max_features='auto',\n",
              "                                                             max_leaf_nodes=250,\n",
              "                                                             max_samples=None,\n",
              "                                                             min_impurity_decrease=0.0,\n",
              "                                                             min_impurity_split=None,\n",
              "                                                             min_samples_leaf=3,\n",
              "                                                             min_samples_split=500,\n",
              "                                                             min_weight_fraction_leaf=0.0,\n",
              "                                                             n_estimators=1000,\n",
              "                                                             n_jobs=-1,\n",
              "                                                             oob_score=False,\n",
              "                                                             random_state=None,\n",
              "                                                             verbose=0,\n",
              "                                                             warm_start=False),\n",
              "                       cv='prefit', method='sigmoid')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "5eSsmef9-V21",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "e9a6ee9b-90fd-4e5e-e4b0-8510ca3888db"
      },
      "source": [
        "i+=1\n",
        "y_pred = cc_model.predict_proba(X_val_200)[:,1]\n",
        "label=\"Random Forest Classifier (top 250)\"\n",
        "labels.append(label)\n",
        "y_preds.append(y_pred)\n",
        "result = plot_auc(y_val,y_pred,label,dataset)\n",
        "result_df = result_df.append(result)\n",
        "del result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\t\t Random Forest Classifier (top 250) on Normal with OHE dataset \t\t \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The log loss and auc was found to be 0.137 and 0.835 for Random Forest Classifier (top 250) on Normal with OHE dataset.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "8Mo702Rt-V23",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "c577e387-b36f-49fe-f17a-b901c3401cc0"
      },
      "source": [
        "result_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Dataset</th>\n",
              "      <th>log_loss</th>\n",
              "      <th>auc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logisic Regression</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.518785</td>\n",
              "      <td>0.824362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.149245</td>\n",
              "      <td>0.771045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.137304</td>\n",
              "      <td>0.832997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>XGBoost Classifer</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.132829</td>\n",
              "      <td>0.846961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LightGBM Classifer</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.133481</td>\n",
              "      <td>0.846349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Random Forest Classifier (top 250)</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.136768</td>\n",
              "      <td>0.835191</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                Model          Dataset  log_loss       auc\n",
              "0                  Logisic Regression  Normal with OHE  0.518785  0.824362\n",
              "1            Decision Tree Classifier  Normal with OHE  0.149245  0.771045\n",
              "2            Random Forest Classifier  Normal with OHE  0.137304  0.832997\n",
              "3                   XGBoost Classifer  Normal with OHE  0.132829  0.846961\n",
              "4                  LightGBM Classifer  Normal with OHE  0.133481  0.846349\n",
              "5  Random Forest Classifier (top 250)  Normal with OHE  0.136768  0.835191"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "i-y4IONn-V26"
      },
      "source": [
        "# XGB Classifier (top 250)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Gjg5rhGa-V27",
        "colab": {}
      },
      "source": [
        "model = xgb.XGBClassifier(n_jobs=-1,\n",
        "                      nthread=-1,\n",
        "                      scale_pos_weight=1,\n",
        "                      learning_rate=0.01,  \n",
        "                      colsample_bytree = 0.6, \n",
        "                      subsample = 0.7,\n",
        "                      objective='binary:logistic', \n",
        "                      n_estimators=710,\n",
        "                      reg_alpha = 0.3,\n",
        "                      max_depth=6, \n",
        "                      gamma=0, random_state=42\n",
        "                     )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "8Q0dR9Kt-V2-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0b4eebb9-3ef2-45a7-96da-c739dd46fcb0"
      },
      "source": [
        "#https://towardsdatascience.com/fine-tuning-xgboost-in-python-like-a-boss-b4543ed8b1e\n",
        "\"\"\"\n",
        "manually changed each parameters 1 by 1 and compared the validation auc\n",
        "and selected the best parameter value\n",
        "\"\"\"\n",
        "eval_metric = ['auc','error']\n",
        "%time model.fit(X_train_200,y_train,eval_set=[(X_val_200,y_val)],eval_metric=eval_metric,verbose=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0]\tvalidation_0-auc:0.801846\tvalidation_0-error:0.040165\n",
            "[1]\tvalidation_0-auc:0.804297\tvalidation_0-error:0.039989\n",
            "[2]\tvalidation_0-auc:0.792752\tvalidation_0-error:0.039902\n",
            "[3]\tvalidation_0-auc:0.806612\tvalidation_0-error:0.039726\n",
            "[4]\tvalidation_0-auc:0.816337\tvalidation_0-error:0.039551\n",
            "[5]\tvalidation_0-auc:0.819756\tvalidation_0-error:0.039551\n",
            "[6]\tvalidation_0-auc:0.81749\tvalidation_0-error:0.039551\n",
            "[7]\tvalidation_0-auc:0.818647\tvalidation_0-error:0.039551\n",
            "[8]\tvalidation_0-auc:0.821015\tvalidation_0-error:0.039551\n",
            "[9]\tvalidation_0-auc:0.823941\tvalidation_0-error:0.039551\n",
            "[10]\tvalidation_0-auc:0.823113\tvalidation_0-error:0.039551\n",
            "[11]\tvalidation_0-auc:0.821829\tvalidation_0-error:0.039551\n",
            "[12]\tvalidation_0-auc:0.822468\tvalidation_0-error:0.039551\n",
            "[13]\tvalidation_0-auc:0.821865\tvalidation_0-error:0.039551\n",
            "[14]\tvalidation_0-auc:0.822539\tvalidation_0-error:0.039551\n",
            "[15]\tvalidation_0-auc:0.824056\tvalidation_0-error:0.039551\n",
            "[16]\tvalidation_0-auc:0.827197\tvalidation_0-error:0.039551\n",
            "[17]\tvalidation_0-auc:0.827212\tvalidation_0-error:0.039551\n",
            "[18]\tvalidation_0-auc:0.826358\tvalidation_0-error:0.039551\n",
            "[19]\tvalidation_0-auc:0.824845\tvalidation_0-error:0.039551\n",
            "[20]\tvalidation_0-auc:0.82573\tvalidation_0-error:0.039551\n",
            "[21]\tvalidation_0-auc:0.825508\tvalidation_0-error:0.039551\n",
            "[22]\tvalidation_0-auc:0.825665\tvalidation_0-error:0.039551\n",
            "[23]\tvalidation_0-auc:0.826242\tvalidation_0-error:0.039551\n",
            "[24]\tvalidation_0-auc:0.826626\tvalidation_0-error:0.039551\n",
            "[25]\tvalidation_0-auc:0.827106\tvalidation_0-error:0.039551\n",
            "[26]\tvalidation_0-auc:0.826505\tvalidation_0-error:0.039551\n",
            "[27]\tvalidation_0-auc:0.825968\tvalidation_0-error:0.039551\n",
            "[28]\tvalidation_0-auc:0.826426\tvalidation_0-error:0.039551\n",
            "[29]\tvalidation_0-auc:0.826181\tvalidation_0-error:0.039551\n",
            "[30]\tvalidation_0-auc:0.826936\tvalidation_0-error:0.039551\n",
            "[31]\tvalidation_0-auc:0.827034\tvalidation_0-error:0.039551\n",
            "[32]\tvalidation_0-auc:0.827278\tvalidation_0-error:0.039551\n",
            "[33]\tvalidation_0-auc:0.828133\tvalidation_0-error:0.039551\n",
            "[34]\tvalidation_0-auc:0.827667\tvalidation_0-error:0.039551\n",
            "[35]\tvalidation_0-auc:0.827895\tvalidation_0-error:0.039551\n",
            "[36]\tvalidation_0-auc:0.827732\tvalidation_0-error:0.039551\n",
            "[37]\tvalidation_0-auc:0.828377\tvalidation_0-error:0.039551\n",
            "[38]\tvalidation_0-auc:0.828229\tvalidation_0-error:0.039551\n",
            "[39]\tvalidation_0-auc:0.828273\tvalidation_0-error:0.039551\n",
            "[40]\tvalidation_0-auc:0.82824\tvalidation_0-error:0.039551\n",
            "[41]\tvalidation_0-auc:0.829057\tvalidation_0-error:0.039551\n",
            "[42]\tvalidation_0-auc:0.829971\tvalidation_0-error:0.039551\n",
            "[43]\tvalidation_0-auc:0.83003\tvalidation_0-error:0.039551\n",
            "[44]\tvalidation_0-auc:0.830203\tvalidation_0-error:0.039551\n",
            "[45]\tvalidation_0-auc:0.829795\tvalidation_0-error:0.039551\n",
            "[46]\tvalidation_0-auc:0.83018\tvalidation_0-error:0.039551\n",
            "[47]\tvalidation_0-auc:0.829971\tvalidation_0-error:0.039551\n",
            "[48]\tvalidation_0-auc:0.829826\tvalidation_0-error:0.039551\n",
            "[49]\tvalidation_0-auc:0.830004\tvalidation_0-error:0.039551\n",
            "[50]\tvalidation_0-auc:0.829819\tvalidation_0-error:0.039551\n",
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            "[55]\tvalidation_0-auc:0.830298\tvalidation_0-error:0.039551\n",
            "[56]\tvalidation_0-auc:0.830495\tvalidation_0-error:0.039551\n",
            "[57]\tvalidation_0-auc:0.830472\tvalidation_0-error:0.039551\n",
            "[58]\tvalidation_0-auc:0.830948\tvalidation_0-error:0.039551\n",
            "[59]\tvalidation_0-auc:0.830825\tvalidation_0-error:0.039551\n",
            "[60]\tvalidation_0-auc:0.83076\tvalidation_0-error:0.039551\n",
            "[61]\tvalidation_0-auc:0.831149\tvalidation_0-error:0.039551\n",
            "[62]\tvalidation_0-auc:0.831421\tvalidation_0-error:0.039551\n",
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            "[65]\tvalidation_0-auc:0.831689\tvalidation_0-error:0.039551\n",
            "[66]\tvalidation_0-auc:0.831964\tvalidation_0-error:0.039551\n",
            "[67]\tvalidation_0-auc:0.831986\tvalidation_0-error:0.039551\n",
            "[68]\tvalidation_0-auc:0.832325\tvalidation_0-error:0.039551\n",
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            "[70]\tvalidation_0-auc:0.832816\tvalidation_0-error:0.039551\n",
            "[71]\tvalidation_0-auc:0.832303\tvalidation_0-error:0.039551\n",
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            "[77]\tvalidation_0-auc:0.833043\tvalidation_0-error:0.039551\n",
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            "[81]\tvalidation_0-auc:0.833195\tvalidation_0-error:0.039551\n",
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            "[88]\tvalidation_0-auc:0.833593\tvalidation_0-error:0.039551\n",
            "[89]\tvalidation_0-auc:0.833746\tvalidation_0-error:0.039551\n",
            "[90]\tvalidation_0-auc:0.833604\tvalidation_0-error:0.039551\n",
            "[91]\tvalidation_0-auc:0.833954\tvalidation_0-error:0.039551\n",
            "[92]\tvalidation_0-auc:0.834024\tvalidation_0-error:0.039551\n",
            "[93]\tvalidation_0-auc:0.834054\tvalidation_0-error:0.039551\n",
            "[94]\tvalidation_0-auc:0.834146\tvalidation_0-error:0.039551\n",
            "[95]\tvalidation_0-auc:0.834156\tvalidation_0-error:0.039551\n",
            "[96]\tvalidation_0-auc:0.833999\tvalidation_0-error:0.039551\n",
            "[97]\tvalidation_0-auc:0.834165\tvalidation_0-error:0.039551\n",
            "[98]\tvalidation_0-auc:0.834404\tvalidation_0-error:0.039551\n",
            "[99]\tvalidation_0-auc:0.834551\tvalidation_0-error:0.039551\n",
            "[100]\tvalidation_0-auc:0.834405\tvalidation_0-error:0.039551\n",
            "[101]\tvalidation_0-auc:0.834509\tvalidation_0-error:0.039551\n",
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            "[103]\tvalidation_0-auc:0.834763\tvalidation_0-error:0.039551\n",
            "[104]\tvalidation_0-auc:0.834745\tvalidation_0-error:0.039551\n",
            "[105]\tvalidation_0-auc:0.834779\tvalidation_0-error:0.039551\n",
            "[106]\tvalidation_0-auc:0.834666\tvalidation_0-error:0.039551\n",
            "[107]\tvalidation_0-auc:0.834745\tvalidation_0-error:0.039551\n",
            "[108]\tvalidation_0-auc:0.834752\tvalidation_0-error:0.039551\n",
            "[109]\tvalidation_0-auc:0.834864\tvalidation_0-error:0.039551\n",
            "[110]\tvalidation_0-auc:0.834716\tvalidation_0-error:0.039551\n",
            "[111]\tvalidation_0-auc:0.834681\tvalidation_0-error:0.039551\n",
            "[112]\tvalidation_0-auc:0.834298\tvalidation_0-error:0.039551\n",
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            "[115]\tvalidation_0-auc:0.834669\tvalidation_0-error:0.039551\n",
            "[116]\tvalidation_0-auc:0.834694\tvalidation_0-error:0.039551\n",
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            "[122]\tvalidation_0-auc:0.835065\tvalidation_0-error:0.039551\n",
            "[123]\tvalidation_0-auc:0.835051\tvalidation_0-error:0.039551\n",
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            "[126]\tvalidation_0-auc:0.835196\tvalidation_0-error:0.039551\n",
            "[127]\tvalidation_0-auc:0.835073\tvalidation_0-error:0.039551\n",
            "[128]\tvalidation_0-auc:0.835021\tvalidation_0-error:0.039551\n",
            "[129]\tvalidation_0-auc:0.834725\tvalidation_0-error:0.039551\n",
            "[130]\tvalidation_0-auc:0.83473\tvalidation_0-error:0.039551\n",
            "[131]\tvalidation_0-auc:0.834749\tvalidation_0-error:0.039551\n",
            "[132]\tvalidation_0-auc:0.835094\tvalidation_0-error:0.039551\n",
            "[133]\tvalidation_0-auc:0.83511\tvalidation_0-error:0.039551\n",
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            "[696]\tvalidation_0-auc:0.84697\tvalidation_0-error:0.039551\n",
            "[697]\tvalidation_0-auc:0.846945\tvalidation_0-error:0.039551\n",
            "[698]\tvalidation_0-auc:0.846965\tvalidation_0-error:0.039551\n",
            "[699]\tvalidation_0-auc:0.84697\tvalidation_0-error:0.039551\n",
            "[700]\tvalidation_0-auc:0.846957\tvalidation_0-error:0.039551\n",
            "[701]\tvalidation_0-auc:0.846987\tvalidation_0-error:0.039551\n",
            "[702]\tvalidation_0-auc:0.846963\tvalidation_0-error:0.039551\n",
            "[703]\tvalidation_0-auc:0.846967\tvalidation_0-error:0.039551\n",
            "[704]\tvalidation_0-auc:0.846966\tvalidation_0-error:0.039551\n",
            "[705]\tvalidation_0-auc:0.846911\tvalidation_0-error:0.039551\n",
            "[706]\tvalidation_0-auc:0.846962\tvalidation_0-error:0.039551\n",
            "[707]\tvalidation_0-auc:0.84694\tvalidation_0-error:0.039551\n",
            "[708]\tvalidation_0-auc:0.846969\tvalidation_0-error:0.039551\n",
            "[709]\tvalidation_0-auc:0.846985\tvalidation_0-error:0.039551\n",
            "CPU times: user 10min 36s, sys: 1.36 s, total: 10min 38s\n",
            "Wall time: 5min 29s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "              colsample_bynode=1, colsample_bytree=0.6, gamma=0,\n",
              "              learning_rate=0.01, max_delta_step=0, max_depth=6,\n",
              "              min_child_weight=1, missing=None, n_estimators=710, n_jobs=-1,\n",
              "              nthread=-1, objective='binary:logistic', random_state=42,\n",
              "              reg_alpha=0.3, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
              "              silent=None, subsample=0.7, verbosity=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "9ZI4N9TM-V3D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "31f1c6b0-b009-4037-dd8e-af3f20670014"
      },
      "source": [
        "y_pred = model.predict_proba(X_val_200)[:,1]\n",
        "label=\"XGBoost Classifer(top 250)\"\n",
        "labels.append(label)\n",
        "y_preds.append(y_pred)\n",
        "i+=1\n",
        "result = plot_auc(y_val,y_pred,label)\n",
        "result_df = result_df.append(result)\n",
        "del result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\t\t XGBoost Classifer(top 250) on Normal with OHE dataset \t\t \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The log loss and auc was found to be 0.133 and 0.847 for XGBoost Classifer(top 250) on Normal with OHE dataset.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "LN2FW-s2-V3G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        },
        "outputId": "cad59dca-e496-4379-9d18-d05b1f360898"
      },
      "source": [
        "result_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Dataset</th>\n",
              "      <th>log_loss</th>\n",
              "      <th>auc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logisic Regression</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.518785</td>\n",
              "      <td>0.824362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.149245</td>\n",
              "      <td>0.771045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.137304</td>\n",
              "      <td>0.832997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>XGBoost Classifer</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.132829</td>\n",
              "      <td>0.846961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LightGBM Classifer</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.133481</td>\n",
              "      <td>0.846349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Random Forest Classifier (top 250)</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.136768</td>\n",
              "      <td>0.835191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>XGBoost Classifer(top 250)</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.133001</td>\n",
              "      <td>0.846985</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                Model          Dataset  log_loss       auc\n",
              "0                  Logisic Regression  Normal with OHE  0.518785  0.824362\n",
              "1            Decision Tree Classifier  Normal with OHE  0.149245  0.771045\n",
              "2            Random Forest Classifier  Normal with OHE  0.137304  0.832997\n",
              "3                   XGBoost Classifer  Normal with OHE  0.132829  0.846961\n",
              "4                  LightGBM Classifer  Normal with OHE  0.133481  0.846349\n",
              "5  Random Forest Classifier (top 250)  Normal with OHE  0.136768  0.835191\n",
              "6          XGBoost Classifer(top 250)  Normal with OHE  0.133001  0.846985"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "mYW7_x88-V3J"
      },
      "source": [
        "# LightGBM Classifier (top 250)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "EYaevQf8-V3J",
        "colab": {}
      },
      "source": [
        "model = lgb.LGBMClassifier(n_jobs=-1,\n",
        "                      nthread=-1,\n",
        "                      scale_pos_weight=1,\n",
        "                      learning_rate=0.01,  \n",
        "                      colsample_bytree = 0.5,\n",
        "                      subsample = 1,\n",
        "                      objective='binary', \n",
        "                      n_estimators=447,\n",
        "                      reg_alpha = 0,\n",
        "                      max_depth=7,\n",
        "                      gamma=0, random_state=42\n",
        "                     )\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "JhKg3-Lv-V3M",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d2d1e582-6dbb-40d9-9307-1f6460ea113a"
      },
      "source": [
        "#https://towardsdatascience.com/fine-tuning-xgboost-in-python-like-a-boss-b4543ed8b1e\n",
        "\"\"\"\n",
        "manually changed each parameters 1 by 1 and compared the validation auc\n",
        "and selected the best parameter value\n",
        "\"\"\"\n",
        "eval_metric = ['auc','error']\n",
        "%time model.fit(X_train_200,y_train,eval_set=[(X_val_200,y_val)],eval_metric=eval_metric,verbose=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1]\tvalid_0's binary_logloss: 0.165872\tvalid_0's auc: 0.786479\n",
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            "[435]\tvalid_0's binary_logloss: 0.133115\tvalid_0's auc: 0.846933\n",
            "[436]\tvalid_0's binary_logloss: 0.133118\tvalid_0's auc: 0.846948\n",
            "[437]\tvalid_0's binary_logloss: 0.13312\tvalid_0's auc: 0.84691\n",
            "[438]\tvalid_0's binary_logloss: 0.133119\tvalid_0's auc: 0.846899\n",
            "[439]\tvalid_0's binary_logloss: 0.133119\tvalid_0's auc: 0.846869\n",
            "[440]\tvalid_0's binary_logloss: 0.13312\tvalid_0's auc: 0.846863\n",
            "[441]\tvalid_0's binary_logloss: 0.133121\tvalid_0's auc: 0.846827\n",
            "[442]\tvalid_0's binary_logloss: 0.13312\tvalid_0's auc: 0.846818\n",
            "[443]\tvalid_0's binary_logloss: 0.133121\tvalid_0's auc: 0.846801\n",
            "[444]\tvalid_0's binary_logloss: 0.133114\tvalid_0's auc: 0.846812\n",
            "[445]\tvalid_0's binary_logloss: 0.133117\tvalid_0's auc: 0.8468\n",
            "[446]\tvalid_0's binary_logloss: 0.133119\tvalid_0's auc: 0.846771\n",
            "[447]\tvalid_0's binary_logloss: 0.133121\tvalid_0's auc: 0.846738\n",
            "CPU times: user 48.1 s, sys: 246 ms, total: 48.3 s\n",
            "Wall time: 25.9 s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=0.5,\n",
              "               gamma=0, importance_type='split', learning_rate=0.01,\n",
              "               max_depth=7, min_child_samples=20, min_child_weight=0.001,\n",
              "               min_split_gain=0.0, n_estimators=447, n_jobs=-1, nthread=-1,\n",
              "               num_leaves=31, objective='binary', random_state=42, reg_alpha=0,\n",
              "               reg_lambda=0.0, scale_pos_weight=1, silent=True, subsample=1,\n",
              "               subsample_for_bin=200000, subsample_freq=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "OFf2WKQC-V3R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4cb9553a-da54-481a-9017-d6c66f8f304d"
      },
      "source": [
        "y_pred = model.predict_proba(X_val_200)[:,1]\n",
        "label=\"LightGBM Classifer(top 250)\"\n",
        "labels.append(label)\n",
        "y_preds.append(y_pred)\n",
        "i+=1\n",
        "result = plot_auc(y_val,y_pred,label)\n",
        "result_df = result_df.append(result)\n",
        "del result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\t\t LightGBM Classifer(top 250) on Normal with OHE dataset \t\t \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "The log loss and auc was found to be 0.133 and 0.847 for LightGBM Classifer(top 250) on Normal with OHE dataset.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F8r0vIjs9i27",
        "colab_type": "text"
      },
      "source": [
        "# Combined"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bfDFKhQ9DajM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "outputId": "9ee1ad6f-6712-40c5-8f63-d08323e6375b"
      },
      "source": [
        "plot_auc(y_val,y_preds,labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FWJqDqYVCUJv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "cca54ac8-cb4f-4f8d-e11d-8bd79e0e8968"
      },
      "source": [
        "result_df.sort_values(by='auc',ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Dataset</th>\n",
              "      <th>log_loss</th>\n",
              "      <th>auc</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>XGBoost Classifer(top 250)</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.133001</td>\n",
              "      <td>0.846985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>XGBoost Classifer</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.132829</td>\n",
              "      <td>0.846961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>LightGBM Classifer(top 250)</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.133121</td>\n",
              "      <td>0.846738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LightGBM Classifer</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.133481</td>\n",
              "      <td>0.846349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Random Forest Classifier (top 250)</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.136768</td>\n",
              "      <td>0.835191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Random Forest Classifier</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.137304</td>\n",
              "      <td>0.832997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logisic Regression</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.518785</td>\n",
              "      <td>0.824362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Decision Tree Classifier</td>\n",
              "      <td>Normal with OHE</td>\n",
              "      <td>0.149245</td>\n",
              "      <td>0.771045</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                Model          Dataset  log_loss       auc\n",
              "6          XGBoost Classifer(top 250)  Normal with OHE  0.133001  0.846985\n",
              "3                   XGBoost Classifer  Normal with OHE  0.132829  0.846961\n",
              "7         LightGBM Classifer(top 250)  Normal with OHE  0.133121  0.846738\n",
              "4                  LightGBM Classifer  Normal with OHE  0.133481  0.846349\n",
              "5  Random Forest Classifier (top 250)  Normal with OHE  0.136768  0.835191\n",
              "2            Random Forest Classifier  Normal with OHE  0.137304  0.832997\n",
              "0                  Logisic Regression  Normal with OHE  0.518785  0.824362\n",
              "1            Decision Tree Classifier  Normal with OHE  0.149245  0.771045"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "lF-jgX2JfCuI"
      },
      "source": [
        "# Saving all the datasets created"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EH0NANEcmKmJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "file_path = '/content/drive/My Drive/Santander_Customer_Satisfaction/'\n",
        "filename = 'normal_train_ohe.pkl'\n",
        "X_train.to_pickle(os.path.join(file_path,'best_models',filename))\n",
        "\n",
        "filename = 'normal_val_ohe.pkl'\n",
        "X_val.to_pickle(os.path.join(file_path,'best_models',filename))\n",
        "\n",
        "filename = 'normal_test_ohe.pkl'\n",
        "X_test.to_pickle(os.path.join(file_path,'best_models',filename))\n",
        "\n",
        "filename = 'normal_train_ohe_250.pkl'\n",
        "X_train_200.to_pickle(os.path.join(file_path,'best_models',filename))\n",
        "\n",
        "filename = 'normal_val_ohe_250.pkl'\n",
        "X_val_200.to_pickle(os.path.join(file_path,'best_models',filename))\n",
        "\n",
        "filename = 'normal_test_ohe_250.pkl'\n",
        "X_test_200.to_pickle(os.path.join(file_path,'best_models',filename))"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}